Grass空投上线却被「反撸」？一览详情及代币估值

区块律动2005-09-24 tarihinde yayınlandı2024-09-05 tarihinde güncellendi

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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.

marsbit19 dk önce

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

marsbit19 dk önce

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.

marsbit1 saat önce

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

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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.

marsbit1 saat önce

Can DeepSeek Save China One Trillion Dollars?

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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.

marsbit1 saat önce

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

marsbit1 saat önce

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1 saat önce

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

华尔街日报1 saat önce

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