2026-08-10 Segunda

Notícias de cripto - Página 449

Mantenha-se a par do mercado de cripto. Notícias em tempo real, análises, preços, histórias em alta e análise de especialistas — tudo num só lugar.

Software Stocks Scared by AI, How Did They Suddenly Become the Hottest Spot in the U.S. Stock Market?

Software stocks, once feared to be doomed by AI, have staged a dramatic comeback, posting their largest two-day outperformance against the S&P 500 in 25 years. Stocks like Snowflake and Datadog surged over 50% in days, fueled by strong earnings that countered earlier panic. The narrative that AI would render traditional software obsolete has flipped. Reports show AI isn't replacing software but becoming a major user of it, requiring databases, security platforms like Okta, and workflow tools. The low hedge fund positioning in software, at a multi-year low, triggered a massive short squeeze and rally. Analysts now argue the profit wave is shifting from AI hardware to software, as companies integrate AI into complex, governed business processes. The key insight is that enterprises pay for the ability to turn AI "intelligence" into reliable, compliant business outcomes—a gap that established software companies are well-positioned to fill. AI isn't killing software; it's redefining its role and creating new demand.

marsbit06/03 02:37

Software Stocks Scared by AI, How Did They Suddenly Become the Hottest Spot in the U.S. Stock Market?

marsbit06/03 02:37

Why Not Short Even When Bearish? Munger Did the Math on a 'Losing Trade'

Why Not Short Even When Bearish? Charlie Munger's Calculated "Loss-Making Account" Many traders, drawn to speculative tools like futures contracts, often face repeated failures. As the article notes, unless one is a genius, such instruments should be avoided for long-term profit-seeking. Similarly, the practice of short selling is viewed with caution. The author firmly states a policy of not shorting, even when bearish, preferring to simply wait. The core reason? Successful short selling requires exceptionally difficult conditions to profit. Legendary investors Warren Buffett and Charlie Munger have themselves reflected on painful short-selling experiences. Munger highlights two critical flaws in the mathematical logic of shorting: 1. Asymmetrical Risk/Reward: A long position has a maximum loss of 100% but unlimited upside. A short position caps profit at 100% (if a stock falls to zero) but carries theoretically unlimited loss potential. 2. The "Promoter" Problem: Fraudulent or struggling companies can prolong their decline. As Munger said, "You can run out of money before the promoter runs out of ideas," meaning short sellers may be forced to cover positions at a loss before the company's true fate unfolds. The article cites Stanley Druckenmiller, a famed hedge fund manager. He once shorted 12 companies that all eventually went bankrupt. However, intense market rallies forced him to cover his positions within three weeks, resulting in massive losses—$200 million of his capital plus an additional $600 million. He concluded he likely never made money shorting in his career. His experience perfectly illustrates Munger's points: facing unlimited losses and being wiped out before being proven right. The conclusion is clear: for most investors, complex instruments like short selling and derivatives are not viable paths to stable, long-term gains. Self-reflection is advised before repeatedly wasting time and capital on such speculative strategies.

marsbit06/03 02:35

Why Not Short Even When Bearish? Munger Did the Math on a 'Losing Trade'

marsbit06/03 02:35

For Hedging, Buy Gold and Oil; For Explosive Growth, Buy AI; Bitcoin, the 'Outdated' Asset, Enters a Bear Market

Bitcoin’s price has recently fallen sharply, hitting a two-month low near $66,000, with Ethereum also dropping to a three-month low. While surface explanations point to ETF outflows, geopolitical tensions, and corporate selling, a deeper issue is emerging: Bitcoin is losing a crucial asset competition. For years, Bitcoin thrived in a low-rate environment where investors sought alternatives amid inflation fears and dissatisfaction with traditional options. Now, the market landscape has shifted, leaving Bitcoin stuck in an "awkward middle ground," facing challenges on three fronts: 1. **As an inflation hedge, gold is winning.** Investors worried about persistent inflation are turning to tangible assets like gold, energy stocks, and commodity producers, which offer more direct pricing power and physical backing. 2. **For growth exposure, AI is winning.** Those seeking high growth now favor AI-related companies with actual revenues and profits, an area where Bitcoin's lack of cash flow puts it at a disadvantage. 3. **Within crypto, infrastructure and stablecoins are winning.** Even investors wanting crypto exposure have alternatives like exchanges, stablecoin issuers, and tokenization firms, whose performance is directly tied to real-world adoption and offers clearer operational leverage. The recent market reaction to inflation warnings highlights this shift. Instead of boosting Bitcoin as "digital gold," such news now drives flows toward traditional inflation-sensitive assets. Therefore, recent events like ETF outflows and corporate selling are seen not as causes, but as symptoms of this new reality. Capital has more compelling options, and investors are becoming more selective. The emerging bear case for Bitcoin is no longer about it being a fraud or failed technology, but rather that **scarcity alone is no longer enough**. It is no longer seen as the best hedge, the best growth asset, or the only crypto play.

marsbit06/03 02:19

For Hedging, Buy Gold and Oil; For Explosive Growth, Buy AI; Bitcoin, the 'Outdated' Asset, Enters a Bear Market

marsbit06/03 02:19

SaaS Battle Royale: The Survivors Who Win All Share One Common Trait

**Summary** The AI revolution has triggered a "SaaS apocalypse," forcing a brutal market shakeout. The key dividing line is the pricing model. Companies like Snowflake and Datadog, which charge based on consumption (e.g., data processed or compute used), are thriving. AI workloads actively *generate* more demand for their services, fueling growth. Datadog's accelerating revenue is a prime example. Microsoft and Palantir, as platform/ecosystem players, also benefit by acting as essential channels for AI deployment. In contrast, traditional SaaS firms built on per-seat or per-task licensing (e.g., Intuit, Adobe) face direct pressure, as AI threatens to automate the very human tasks their software supports. Companies like Salesforce, a per-seat giant, are caught in the middle. While showing strong AI monetization (e.g., its Agentforce platform) and experimenting with consumption-based "Flex Credits," its stock remains under pressure, illustrating that the market rewards *completed* transitions, not just the intent. The recent Microsoft Build conference underscored key trends: AI is evolving from an assistant to an autonomous "agent," and platform providers like Microsoft are consolidating their control. The market's recovery is highly selective, focused on identifying which companies are "fed by AI" versus "eaten by AI." Future focus will be on the diffusion of this recovery to transforming companies and the real-world adoption data of AI agents like Microsoft Copilot.

marsbit06/03 02:02

SaaS Battle Royale: The Survivors Who Win All Share One Common Trait

marsbit06/03 02:02

XRP Falters In Face Of Rival XLM: Why Retail Traders Are Dumping One For The Other

Rival cryptocurrencies XRP and XLM are seeing a shift in retail trader preference, particularly in South Korea. Data from the Upbit exchange shows XLM's 24-hour trading volume ($252.3M) has surpassed XRP's ($125.7M) for the first time. This shift is attributed to increased interest in Stellar's role in tokenized finance, fueled by news linking the network to the US DTCC. While XLM's price surged over 55% to $11, XRP fell below $1.3 despite its historical popularity in Asian markets. A separate analyst suggests XRP could follow with a significant rally, potentially targeting new highs, though this remains speculative.

bitcoinist06/03 02:01

XRP Falters In Face Of Rival XLM: Why Retail Traders Are Dumping One For The Other

bitcoinist06/03 02:01

Trend in US Stocks: Jensen Huang's One Sentence Triggers $47 Billion Surge; Google Raises Funds for First Time in 20 Years

U.S. markets reached record highs on June 2nd, but the real story was the intensifying AI arms race, now pivoting from chip supremacy to a scramble for capital to fund compute infrastructure. The day highlighted two stark realities: Nvidia CEO Jensen Huang's endorsement of Marvell Technology as the "next trillion-dollar company" at Computex fueled a historic 32.5% surge, adding $47 billion to its value. Conversely, Alphabet announced its first equity raise in two decades—an $80 billion plan—signaling that even its massive cash flow can't keep pace with soaring AI capital expenditures, forecast to exceed $180 billion in 2026. While the S&P 500 closed above 7,600 for the first time, led by tech and semiconductor stocks (SOXX +5.79%), sector performance was mixed. Alphabet's 4% drop dragged down communications services, illustrating market anxiety over the unsustainable cost of the AI buildout. Hewlett Packard Enterprise soared 25% on stellar earnings, proving AI's benefits extend beyond chip designers to infrastructure providers. Beneath the index highs, concerns linger over extreme concentration in a few AI stocks and geopolitical tensions. The focus now shifts to upcoming economic data, particularly Friday's nonfarm payrolls, which could challenge the market's current "ignore rates, chase AI" mentality.

marsbit06/03 01:31

Trend in US Stocks: Jensen Huang's One Sentence Triggers $47 Billion Surge; Google Raises Funds for First Time in 20 Years

marsbit06/03 01:31

Variant: Bitcoin, Ethereum, and ZCash Highly Likely to Become Major Stores of Value

At Variant, our investment philosophy centers on enabling individuals to own their money, identity, and data. We believe all tokens fall into two categories: stores of value (SOV) or equity-like instruments. The SOV framework is particularly useful for evaluating Layer 1 (L1) blockchains, which are major monetary coordination protocols. A good store of value should possess: 1) Technical durability, 2) Scarcity, 3) Censorship resistance, 4) Economic productivity, 5) Strong memetics, and 6) Liquidity. The total addressable market for SOV assets is massive, with gold alone valued at $31 trillion. Three L1 assets stand out as prime candidates for becoming major stores of value: Bitcoin (BTC), Ethereum (ETH), and ZCash (ZEC). Bitcoin excels in memetic dominance as "digital gold." Ethereum demonstrates superior technical durability and adaptability through its upgradeable roadmap. ZCash offers exceptional censorship resistance and privacy via its shielded transactions. While digital SOVs outperform traditional ones like gold on many metrics, they still represent a small fraction of the overall SOV market, presenting a significant growth opportunity.

marsbit06/03 00:48

Variant: Bitcoin, Ethereum, and ZCash Highly Likely to Become Major Stores of Value

marsbit06/03 00:48

Can DeepSeek Save China One Trillion Dollars?

"DeepSeek and the $1 Trillion Infrastructure Question" The article examines whether DeepSeek's AI optimization breakthroughs could potentially save China $1 trillion in future AI infrastructure costs. The analysis begins with Nvidia's upcoming Vera Rubin AI platform, costing ~$7.8 million, where memory (HBM4/LPDDR5X) constitutes $2 million—a 435% cost increase in one year, highlighting how AI hardware spending is shifting toward expensive memory components. DeepSeek's approach works in the opposite direction. Through three key technical innovations showcased in DeepSeek V4, the company dramatically improves hardware efficiency: 1. **Memory Compression (MLA)**: Re-engineers the attention mechanism to compress long-context memory (KV Cache) by over 90%, drastically reducing expensive HBM usage. 2. **Selective Activation (MoE)**: Employs Mixture-of-Experts architecture where only a small fraction of parameters (e.g., 49B out of 1.6T in V4-Pro) are activated per token, allowing most parameters to reside in cheaper memory/SSD. 3. **Computation Caching**: Reuses previously computed results via cache hits, replacing expensive GPU computations with cheap memory reads. Combined, these optimizations allow the same hardware to produce approximately 4x more tokens, effectively reducing required hardware investment by 75%. DeepSeek's pricing reflects this: a 10-billion token workload costs ~$522 monthly versus ~$9,000-$10,000 for competitors. The $1 trillion savings projection stems from McKinsey's estimate that global AI infrastructure will require ~$5.2 trillion investment by 2030. As China's daily token consumption grows toward quadrillions, even marginal efficiency gains scale massively. With a conservative 4x throughput improvement, China could avoid building tens of thousands of AI data centers equivalent to ~7 trillion RMB ($1 trillion) in saved investment. Critically, this strategy shifts dependency from scarce, expensive GPU/HBM—where China lags—toward more accessible storage, caching, and systems engineering where domestic suppliers like CXMT are gaining strength. Rather than "replacing Nvidia," DeepSeek rebalances AI's value chain away from monolithic hardware dependency. Ultimately, DeepSeek's technical breakthroughs could lower the barrier to AI adoption across Chinese industries by making advanced capabilities affordable at scale—transforming who can access next-generation AI.

marsbit06/03 00:47

Can DeepSeek Save China One Trillion Dollars?

marsbit06/03 00:47

Overturning the Mainstream Approach to Hallucinations: Metacognition is the New Solution for Large Models to Break the Hallucination Barrier

This paper, "Hallucinations Undermine Trust; Metacognition is a Way Forward," proposes a paradigm shift in combating AI hallucination. It argues that the current mainstream approaches—striving for omniscience by scaling data/models or having AI abstain from uncertain answers—are fundamentally flawed. The former has inevitable knowledge gaps, while the latter imposes a crippling "utility tax," requiring the rejection of many correct answers to achieve high accuracy, due to models' poor "discrimination" (the ability to distinguish correct from incorrect answers internally). The core contribution is redefining hallucination not as "being wrong," but as "expressing false information with unwarranted certainty." The proposed solution is **Faithful Uncertainty** or **Metacognition**: enabling AI to accurately perceive its internal uncertainty and honestly express it in its language (e.g., using hedging phrases when unsure). This creates a more reliable assistant that provides useful information while signaling its confidence, minimizing harm from errors. The paper emphasizes that metacognition is critical for the era of AI Agents. Without it, Agents cannot intelligently decide when to use tools like search engines, leading to inefficiency and misuse. Key implementation challenges are highlighted: the "bootstrapping paradox" of training with static uncertainty data, the "alignment distortion signal" where human preference training suppresses internal uncertainty cues, and the difficulty of causally evaluating true metacognition vs. its superficial imitation. The paper concludes that the goal should not be an infallible AI, but one that is honest about the limits of its knowledge, thereby building user trust through transparent communication of its certainty.

marsbit06/03 00:43

Overturning the Mainstream Approach to Hallucinations: Metacognition is the New Solution for Large Models to Break the Hallucination Barrier

marsbit06/03 00:43

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

Bitcoin has recently declined, hitting a two-month low near $66,123, while Ethereum fell to a three-month low around $1,837. Analysts suggest the drop is not merely due to factors like ETF outflows or MicroStrategy's selling but reflects a deeper issue: Bitcoin is losing a broader asset competition. In a near-zero interest rate environment, Bitcoin previously thrived as an outlet for investor dissatisfaction with inflation and limited options. However, the market landscape has shifted. Bitcoin now occupies an "awkward middle ground," facing competition on three fronts. For inflation hedging, investors prefer gold, energy stocks, and commodity producers—assets with tangible backing and clearer pricing power. For growth exposure, AI-related companies with actual revenues and profits are more attractive. Even within crypto, investors can choose stablecoins, exchanges, or infrastructure firms tied directly to adoption, offering clearer business models and leverage. Thus, Bitcoin is no longer the top choice for hedging, growth, or crypto exposure. This shift is evident in market reactions: despite recent warnings about persistent inflation from a Fed official, Bitcoin did not rally as it might have in the past. Instead, capital flowed to assets with direct commodity or energy exposure. The recent ETF outflows and MicroStrategy sales are symptoms, not causes, of this new reality. Investors are becoming more selective, demanding clearer value propositions beyond mere scarcity. The emerging bear case for Bitcoin is not about it being a bubble or failed technology, but that scarcity alone is no longer sufficient.

华尔街日报06/03 00:40

Hedge by Buying Gold and Oil, Chase Soaring Returns with AI. ‘Dated’ Bitcoin Enters a Bear Market

华尔街日报06/03 00:40

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