Цена золота обновила исторический максимум — биткоин укрепляется как «цифровое золото»

cryptonews.ruPublished on 2022-04-12Last updated on 2025-04-12

Пока мир штормит из-за очередного витка торговых пошлин, золото, как настоящий ветеран кризисов, снова показало отличную динамику. Его цена вылетела на новый исторический максимум — $3,200 за унцию. Те, кто заранее вложился в драгоценный металл, сейчас явно потирают руки — доходность улетела в космос.

На фоне паники на рынках активы-убежища, вроде золота, снова вошли в моду и пользуются бешеным спросом.

Авторитетный аналитик CryptoQuant Алекс Адлер младший подметил интересную деталь: биткоин начинает всё активнее примерять на себя роль «золота 2.0«. За год BTC подтянулся в цене на бодрые 16%, закрепляя за собой статус защиты от экономических катаклизмов.

По словам того же Адлера, основным триггером резкого скачка цен на золото стала свежая выходка США — очередное повышение тарифов на импорт из Китая. Новая планка пошлин — это какой-то ультимативный уровень: плюс 125% к действующим ставкам, что довело общий тариф до заоблачных 145%.

В то же время для других стран власти США включили режим «передышки» — дали 90-дневную отсрочку от новых пошлин. Но Китай оказался главным мишенью — рынки тут же отреагировали нервно.

Адлер считает, что усиление тарифной войны разогрело страхи инвесторов перед экономическим завалом. Особенно на фоне того, что доллар теряет свои позиции. Поэтому золото стало настоящим магнитом для капитала, спасаясь от будущих потрясений.

Золото всегда умело удерживать статус надёжного укрытия от инфляции и прочих финансовых напастей. С начала года цена на жёлтый металл выросла больше чем на 20% — не в последнюю очередь благодаря массовым закупкам со стороны иностранных центробанков.

Если торговая война будет только разрастаться, Адлер прогнозирует, что ценник на золото может ещё не раз удивить новыми высотами.

Пока весь мир следит за ростом цен на физическое золото, биткоин тоже показывает силу. За последний год крипта прибавила крепкие 16% — несмотря на общую турбулентность рынков.

На данный момент BTC немного подрос и торгуется в районе $81,910. Да, это всё ещё ниже исторического максимума в $109 тысяч, но динамика говорит сама за себя — инвесторы всё активнее добавляют его в свой набор защитных активов наряду с золотом.

20-Year-Old Founder Hires 18-Year-Old Employee, Funded by 19-Year-Old

The AI industry is experiencing a radical youth movement. Companies are aggressively recruiting extremely young talent—interns as young as 17 with daily salaries up to $800, and recent undergraduates earning annual packages of $200k-$1M. This shift prioritizes being "AI Native"—a mindset more common in the post-2000 generation—over traditional experience. Major tech firms like ByteDance, Tencent, and Alibaba restructure to empower young researchers directly, finding that past management expertise can hinder innovation in this fast-paced field. This creates a self-reinforcing cycle: young researchers gain authority, hire peers, attract young investors, and start companies, collectively building a new power network that sidelines older professionals. The intense competition for a limited pool of top young minds has led to astronomical salaries, defensive hiring, and sophisticated early talent scouting from high school. However, this concentration of opportunity exacerbates inequality, leaving many experienced workers facing obsolescence as AI disrupts traditional tech roles. While rewarding "extraordinary" talent unprecedentedly, the industry is simultaneously delivering a harsh penalty for those perceived as ordinary or tied to outdated methods.

marsbit5m ago

20-Year-Old Founder Hires 18-Year-Old Employee, Funded by 19-Year-Old

marsbit5m ago

Micron Q2 Earnings Preview: Citi Raises Price Target

TL;DR: Ahead of Micron's Q2 FY26 earnings report on June 24, Citi raised its price target from $840 to $1,200, citing stronger-than-expected 2026 memory prices and high gross margins. The stock price had already neared $1,211, shifting focus to whether the aggressive earnings assumptions will hold. The $1,200 target is based on significantly higher FY27 EPS estimates ($114.73), driven by projections of DRAM ASPs rising 200% and NAND ASPs rising 186% in 2026. The bullish outlook hinges on the ongoing transition of spot price increases (DRAM up 52% YTD) to contract prices, with server demand being a key driver. Supply remains tight, with a projected 5% DRAM shortage in 2026, exacerbated by HBM production consuming capacity. Long-Term Agreements (LTAs) could provide stability but their specific terms are crucial. Risks include HBM yield/ramp challenges, potential industry overcapacity, and AI/data center capex fluctuations. With the stock near the target, future moves depend on the continuation of strong pricing, tight supply, and high margins.

marsbit10m ago

Micron Q2 Earnings Preview: Citi Raises Price Target

marsbit10m ago

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.

marsbit16m ago

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

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Having Raised $37 Million with a16z's Backing, Why Did Goldfinch Ultimately Fail?

The decentralized credit protocol Goldfinch, which raised $37.7 million in funding led by Andreessen Horowitz (a16z), is officially entering a shutdown process. After six years of operation and issuing approximately $100 million in loans, it faces widespread borrower defaults and frozen depositor funds. Depositors report an estimated actual loss rate of around 70%, with the protocol's governance token GFI having plummeted 99.8% from its all-time high. The current proposal involves winding down operations, halting new development, and establishing a trust to manage loan recovery over an estimated two-plus years. Founded by ex-Coinbase employees, Goldfinch aimed to channel crypto capital into loans for emerging markets via local lenders. However, it suffered significant defaults from major borrowers like Tugende Kenya, Stratos, and Lend East, leading to over $18 million in losses. The failure highlights the core challenge for DeFi-based Real-World Asset (RWA) lending: while blockchain technology improves capital fluidity, it cannot replace the critical offline components of credit underwriting, collateral management, and legal enforcement, especially in high-risk jurisdictions.

Foresight News20m ago

Having Raised $37 Million with a16z's Backing, Why Did Goldfinch Ultimately Fail?

Foresight News20m ago

Rented Faith: How Much of the Bitcoin ETF Inflows Is Real Money?

"Rented Conviction: How Much of Bitcoin ETF Flows Is Real Money" The weekly inflows into Bitcoin ETFs are often interpreted as a gauge of institutional belief. However, a significant portion of this activity is driven by a hidden arbitrage trade, not directional conviction. The core mechanism is a cash-and-carry arbitrage: traders buy spot Bitcoin (often via ETFs) while simultaneously shorting CME futures to lock in the price difference, or "basis." This delta-neutral trade is essentially an interest rate play. In weekly data, about half the fluctuation in ETF flows can be explained by new short positions added by leveraged funds (hedge funds), with a correlation of 0.70. Bitcoin's price movement in a given week shows no statistical power in predicting these flows. While this arbitrage trade drives weekly *volatility*, it is not the main component of the cumulative *stock*. Of the total ~$55 billion in net ETF inflows, the current net arbitrage position is only about $1 billion. The remainder is steady, directional buying averaging ~$400 million per week, which constitutes the vast majority of the accumulated "mountain" over two years. Thus, ETF flow data overstates the *volatility* of conviction, not its *level*. This arbitrage trade has been unwinding for nearly two years. Leveraged fund short positions peaked at ~$14 billion in late 2024 and have since declined to ~$4.5 billion. When the basis compresses to unprofitable levels, ETF inflows and short positions retreat together. Recent outflows should not be mistaken for a loss of faith but rather the routine unwinding of this rate trade. For Ethereum ETFs, the pattern is weaker. Accounting for staking yield makes the basis often negative, so neither strong conviction buying nor robust arbitrage supports its flows. To interpret ETF flows correctly, monitor the CME basis versus T-bill rates and leveraged fund net shorts. They reveal how much of the next "demand" headline is real. The real, patient buy-and-hold demand is what constitutes the enduring bulk of ETF assets.

marsbit1h ago