Нейросеть Илона Маска рассказала, будет ли продолжение буллрана

cryptonews.ruОпубліковано о 2025-04-19Востаннє оновлено о 2025-08-20

Участники криптосообщества продолжают советоваться с нейросетями. На этот раз обратились к ИИ Илона Маска Grok с просьбой прокомментировать ближайшие перспективы крипторынка

Специалист по стратегическому планированию Нил Джейкобс попросил нейросеть ответить на вопрос о том, завершен ли буллран. ИИ дал аргументированный ответ.

Джейкобс обратился к Grok с просьбой ответить на вопрос о том, действительно ли бычий рынок биткоина закончился. Он попросил, чтобы при формировании ответа ИИ опирался исключительно на собственный анализ ситуации, не ссылаясь на сторонние прогнозы.

В начале Grok обратил внимание на пункты, которые лежат в основе его анализа. Он отметил, что 14 августа биткоин обновил абсолютный максимум, после чего началась коррекция. Падение 19 августа составило около 2,8% за сутки. Изменения происходят на фоне сильного восходящего тренда 2025 года. С начала года BTC вырос более чем на 100% благодаря институциональному принятию и сокращению предложения после халвинга.

«Бычьи рынки сопровождаются коррекциями. Текущее снижение на 9% сопоставимо с прошлыми откатами, например, 20–30% в бычьем рынке 2021 года, и не означает завершения цикла. Ключевая поддержка на уровнях $110 000–$112 000 удерживается, объем торгов в размере $71 млрд указывает на устойчивый интерес, а дефицит (в обращении 19,9 млн BTC) подтверждает долгосрочную ценность», — пишет Grok.

Проанализировав совокупность доступных данных, ИИ пришел к однозначному выводу о том, что бычий тренд не завершен. По его мнению, BTC просто консолидируется.

«Волатильность сохраняется, но восходящий импульс, вероятно, возобновится при снижении макроэкономических рисков», — подытожила нейросеть Илона Маска.

Напомним, ранее Grok дал прогноз биткоина на 100 дней. К сожалению, анализ ответов показал, что нейросеть Илона Маска часто путается в показаниях. Дело в том, что ответ ИИ сильно зависит от того, как поставлена задача. Различия в двух, на первый взгляд практически идентичных промптах, могут привести к разной реакции. Поэтому в сети можно встретить различные вариации прогнозов Grok по схожим запросам.

Есть еще одна проблема. Опыт, который провела редакция BeInCrypto, показал, что часто нейросеть Илона Маска дает слишком оптимистичные прогнозы.

Пов'язані матеріали

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.

marsbit3 хв тому

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?

marsbit3 хв тому

Those Who Wanted to Buy Have Already Bought: SpaceX Retail Frenzy Subsides, Real Selling Pressure Arrives in August

SpaceX (SPCX) debuted on June 12 at $150, soared to a record $225 by June 16 in a retail-fueled frenzy, then collapsed, losing over $600 billion in market cap in a single day this week and crashing back near its IPO price. Analysts note the initial surge was driven by record retail inflows exceeding those for major tech stocks, but momentum evaporated swiftly after the peak. The sell-off occurred with only 5% of shares freely tradable. The real pressure is yet to come: lock-up expirations starting in August could release up to 44% of insider shares by early September, potentially increasing the float ninefold. With retail buying exhausted and massive supply looming, analysts warn the path higher is now severely challenged, posing a broader risk to a market sustained by speculative momentum.

marsbit8 хв тому

Those Who Wanted to Buy Have Already Bought: SpaceX Retail Frenzy Subsides, Real Selling Pressure Arrives in August

marsbit8 хв тому

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.

marsbit12 хв тому

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

marsbit12 хв тому

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.

marsbit17 хв тому

Micron Q2 Earnings Preview: Citi Raises Price Target

marsbit17 хв тому

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.

marsbit23 хв тому

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

marsbit23 хв тому

Торгівля

Спот

Ф'ючерси