# Artikel Terkait Data Centers

$700 Billion Poured into AI, Americans Taste the Bitter Fruit of Inflation First

A Federal Reserve analysis from the St. Louis Fed argues that AI optimism itself is a driver of inflation. The "news shock" of AI's revolutionary potential causes households and businesses to increase spending and investment in anticipation of future gains, pushing demand beyond current supply and creating inflationary pressure. This is supported by a Deutsche Bank experiment where AI models (dbLumina, Claude, ChatGPT-5.2) assessed a 20-40% probability that AI would raise inflation in the next year, citing surging demand for data centers, semiconductors, and electricity. They saw only a 5% chance of AI significantly reducing inflation. Massive capital expenditure underscores this demand. Amazon, Microsoft, Google, and Meta are projected to spend a combined ~$663B in 2026, a fourfold increase in four years. A significant portion funds power-hungry data centers. For example, OpenAI's "Stargate" project plans a 10-gigawatt capacity, equivalent to the entire electricity load of 16 Vermont states. U.S. data center electricity consumption is forecast to triple by 2030. While AI could eventually boost productivity and be disinflationary long-term, current data shows no such productivity jump. The U.S. economy now faces a cycle: massive AI investment fuels inflation, delays interest rate cuts, raises financing costs—yet the investment continues to accelerate. The outcome hinges on whether these AI models will ultimately make the economy more efficient, a question that remains unanswered.

marsbit04/02 11:03

MARA’s pivot to AI is net positive for Bitcoin, experts believe – Here’s why

Public Bitcoin miners like Marathon Digital (MARA) are increasingly shifting resources toward AI, a move analysts see as beneficial for the remaining Bitcoin mining ecosystem. While this pivot reduces the overall network hashrate, it leads to a decrease in mining difficulty. This, in turn, improves profit margins for the solo and medium-sized miners who continue operating. MARA recently sold over $1 billion in BTC to reduce debt and fund its new AI data center venture, a trend followed by other major miners. The current cycle shows the lowest network difficulty growth as a result. Analysts suggest this creates a more balanced ecosystem with less hash rate controlled by large public companies. Furthermore, a potential energy price increase, triggered by an extended closure of the Strait of Hormuz, could disadvantage oil-dependent miners and further benefit smaller operations with stable power agreements. Although miner distress eased in early March, supporting BTC's price recovery, a drop below $65,000 could trigger another wave of miner sell-offs and put downward pressure on the price.

ambcrypto03/29 07:01

Losing $19,000 Per Mined Coin, Bitcoin Mining Companies Collectively Flee to AI

Facing unsustainable losses of approximately $19,000 per Bitcoin mined due to rising costs and stagnant BTC prices, major public mining firms are pivoting aggressively toward AI infrastructure. According to a CoinShares report, these companies have signed over $70 billion in AI and high-performance computing contracts, with AI revenue projected to reach 70% of total income by 2026. To fund this transition, miners are taking on significant debt and selling Bitcoin holdings—over 15,000 BTC have been liquidated collectively. This shift is reducing Bitcoin’s network security, as hash rate has dropped from 1,160 EH/s to 920 EH/s. Market valuations now favor AI-exposed miners at 12.3x forward revenue, compared to 5.9x for pure-play miners. The industry’s future depends heavily on Bitcoin’s price: a recovery to $100,000 could slow the AI transition, while prices below $70,000 may accelerate the move away from mining.

marsbit03/29 04:33

Is AI Killing Bitcoin Mining? Here’s The Truth

A new debate suggests AI data centers, as richer buyers of electricity, could threaten Bitcoin mining by outbidding for power. Crypto influencer Ran Neuner argued AI has "killed Bitcoin" by competing for scarce energy, citing higher revenue per megawatt ($200–$500 for AI vs. $57–$129 for Bitcoin) and miner pivots to AI. However, analysts push back. Willy Woo emphasizes Bitcoin’s adaptive difficulty adjustment: if high-cost miners exit, the network adjusts, maintaining security. Daniel Batten calls the claim "nonsense," arguing AI may actually depend on Bitcoin mining for energy monetization during data center construction, using stranded energy, and smoothing demand patterns. He stresses mining economics are nuanced, involving variable costs, heat recycling, and renewable energy opportunities. The article concludes that AI may reshape mining but won’t kill Bitcoin.

bitcoinist03/16 16:32

From Power to Chips: How Ordinary People Can Participate in the Wealth Opportunities of the AI Era

From Power to Chips: How Ordinary People Can Participate in the Wealth Opportunities of the AI Era This article analyzes the AI industry through a five-layer "AI stack" framework: energy, chips, cloud infrastructure, models, and applications. It argues that while public attention focuses on the top application layer (e.g., ChatGPT), the vast majority of capital investment and profits are currently concentrated in the underlying infrastructure layers. Key points include: - An estimated $700 billion in annual capital expenditure is flowing into AI infrastructure (energy, chips, data centers), not applications. - Infrastructure companies (Nvidia, TSMC, ASML) show massive profits and near-monopolies, while model companies (OpenAI, Anthropic) experience rapid revenue growth but burn enormous cash due to compute costs. - Historical parallels are drawn to the electricity revolution and internet infrastructure boom, where infrastructure builders captured most early value. - The article advises investors to focus on infrastructure layers currently generating concentrated profits, while acknowledging future value may shift to applications as the market matures. - Risks include capital misallocation, supply chain concentration, and efficiency breakthroughs (like DeepSeek's lower-cost models) that could disrupt current assumptions. The conclusion emphasizes understanding this layered structure, tracking capital flow, and participating at appropriate levels based on risk tolerance and expertise.

marsbit03/16 08:17

Strongest Earnings Report in 15 Years Fails to Mask Trillion-Dollar Debt; Oracle Rumored to Lay Off 30,000 in 'AI Replacement' Move—Can It Fill the Computing Power Pit?

Oracle reported its strongest financial results in 15 years, with Q3 revenue reaching $17.2 billion, a 22% year-over-year increase, and cloud revenue surging 44%. The company's remaining performance obligations (RPO) grew 325% to $553 billion. Despite these gains, Oracle faces significant financial challenges, including negative free cash flow of -$13.18 billion over the past 12 months and total debt exceeding $100 billion, with an additional $248 billion in off-balance-sheet lease commitments. To fund its aggressive data center expansion—with capital expenditures projected to reach $50 billion this year—Oracle is reportedly planning to lay off up to 30,000 employees. Analysts estimate these cuts could save the company $8–10 billion in free cash flow. The shift toward an asset-light “AI infrastructure management” model, where clients prepay or supply their own GPUs, reduces balance sheet pressure but also transforms Oracle into a lower-margin service operator. Competitive pressures are mounting: key clients like OpenAI have canceled expansion plans due to rapid chip obsolescence, as NVIDIA’s new Vera Rubin chips offer significantly better performance. This reflects a broader industry trend where tech giants are cutting jobs to fund AI investments, transferring the cost of technological advancement onto their workforce.

marsbit03/11 05:57

Miners Stop Mining Bitcoin, Sell Electricity to AI

Amid a severe mining profitability crisis in early 2026, with Bitcoin's price below production costs, major mining firms are strategically pivoting from Bitcoin mining to selling power to AI data centers. Publicly traded miners, including Core Scientific, Marathon Digital (MARA), and Bitdeer, have collectively sold over 15,000 BTC to fund this transition. They are repurposing their energy-intensive operations into high-density AI computing facilities, capitalizing on the explosive demand for AI inference workloads, which offers more stable, long-term dollar-denominated contracts compared to the volatility of crypto mining revenues. Financial analysts note that shifting power from mining to AI can increase asset valuation by over 10x. While this mass sell-off has contributed to negative market sentiment and some of the longest "miner capitulation" periods on record, on-chain metrics simultaneously suggest Bitcoin may be nearing a cyclical bottom. Ultimately, this shift transforms these companies from perpetual Bitcoin sellers into neutral or even potential future buyers, strengthening the Bitcoin network's long-term health by removing structural selling pressure and creating a more efficient mining ecosystem.

marsbit03/11 03:42

Who is Making Money in the AI Era? The Must-See Investment List for HALO Asset Trading

The article examines who profits in the AI era, revisiting Daniel Gross's 2024 questions on AI's impact. It finds value is concentrated in infrastructure: Nvidia, with a $3.2 trillion market cap increase, captured most profits, while cloud providers like Microsoft saw modest gains. Energy emerged as a critical bottleneck, with nuclear and utility stocks surging. Copper demand soared due to data centers, while coal saw mixed results. Geopolitically, the US leads in AI investment and model development. API costs plummeted 50x, driving deflation in knowledge production, but overall SaaS revenues grew. Key bottlenecks include chip packaging and power transformers. The piece also discusses AI's uneven impact on employment, regional real estate, and the fragile balance in semiconductor supply chains, particularly on Taiwan.

marsbit03/06 06:41

Cheaper to mine, harder to profit – The new reality for Bitcoin miners!

ambcrypto02/28 08:02

How Do Chinese AI Models Use Tokens to 'Export' Electricity?

The article explores how Chinese AI models are effectively "exporting" electricity globally through token consumption. In 2026, data from OpenRouter shows that Chinese models account for 61% of the top ten models' token usage, with MiniMax M2.5, Kimi K2.5, and Zhipu GLM-5 leading. This shift is driven by the rise of tools like OpenClaw, which exponentially increases token usage, and the significantly lower cost of Chinese models compared to Western alternatives (e.g., MiniMax at $0.3 per million input tokens vs. Claude Opus at $5). The core idea is that token consumption represents the export of computational power and electricity. When a developer overseas sends an API request, it is processed in Chinese data centers using China’s cheaper electricity (about 40% lower than the U.S.), without the power physically leaving the country. This creates a form of invisible, tariff-free "export" of energy. The trend mirrors Bitcoin mining, where China once dominated by converting cheap electricity into digital value, but AI tokens offer more utility and deeper integration into global workflows. However, challenges like data sovereignty and chip restrictions remain. Ultimately, the token economy is becoming a new frontier in U.S.-China tech competition, with Chinese models gaining influence by embedding into developers' systems worldwide.

比推02/26 13:05