tokenomics
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X @Michaël van de Poppe
Michaël van de Poppe· 2025-10-01 16:30
$VET has been delivering.Now, the reason why I think that price is going to surge and break out from this construction is also due to the fact that the tokenomics are adjusting.Why?To make it fair and, potentially, deflationary.The $VTHO token will only be earned through staking and other activities in the ecosystem, enhancing a positive flow within the ecosystem.In that way, the community should be continuing to grow as there is lower inflation within the ecosystem.I think that this is still one to break o ...
SemiAnalysis创始人Dylan最新访谈--AI、半导体和中美
傅里叶的猫· 2025-10-01 14:43
Core Insights - The article discusses the insights from a podcast featuring Dylan Patel, founder of SemiAnalysis, focusing on the semiconductor industry and AI computing demands, particularly the collaboration between OpenAI and Nvidia [2][4][20]. OpenAI and Nvidia Collaboration - OpenAI's partnership with Nvidia is not merely a financial arrangement but a strategic move to meet its substantial computing needs for model training and operation [4][5]. - OpenAI has 800 million users but generates only $1.5 to $2 billion in revenue, facing competition from trillion-dollar companies like Meta and Google [4][5]. - Nvidia's investment of $10 billion in OpenAI aims to support the construction of a 10GW cluster, with Nvidia capturing a significant portion of GPU orders [5][6]. AI Industry Dynamics - The AI industry is characterized by a race to build computing clusters, where the first to establish such infrastructure gains a competitive edge [7]. - The risk for OpenAI lies in its ability to convert its investments into sustainable revenue, especially given its $30 billion contract with Oracle [6][20]. Model Scaling and Returns - Dylan argues against the notion of diminishing returns in model training, suggesting that significant computational increases can lead to substantial performance improvements [8][9]. - The current state of AI development is likened to a "high school" level of capability, with potential for growth akin to "college graduate" levels [9]. Tokenomics and Inference Demand - The concept of "tokenomics" is introduced, emphasizing the economic value of AI outputs relative to computational costs [10][11]. - OpenAI faces challenges in maximizing its computing capacity while managing rapidly doubling inference demands every two months [10][11]. Reinforcement Learning and Memory Mechanisms - Reinforcement learning is highlighted as a critical area for AI development, where models learn through iterative interactions with their environment [12][13]. - The need for improved memory mechanisms in AI models is discussed, with a focus on optimizing long-context processing [12]. Hardware, Power, and Supply Chain Issues - AI data centers currently consume 3-4% of the U.S. electricity, with significant pressure on the power grid due to the rapid growth of AI infrastructure [14][15]. - The industry is facing labor shortages and supply chain challenges, particularly in the construction of new data centers and power generation facilities [17]. U.S.-China AI Stack Differences and Geopolitical Risks - Dylan emphasizes that without AI, the U.S. risks losing its global dominance, while China is making long-term investments in various sectors, including semiconductors [18][19]. Company Perspectives - OpenAI is viewed positively but criticized for its scattered focus across various applications, which may dilute its execution capabilities [20][21]. - Anthropic is seen as a strong competitor due to its concentrated efforts in software development, particularly in the coding market [21]. - AMD is recognized for its competitive pricing but lacks revolutionary breakthroughs compared to Nvidia [22]. - xAI's potential is acknowledged, but concerns about its business model and funding challenges are raised [23]. - Oracle is positioned as a low-risk player benefiting from its established cloud business, contrasting with OpenAI's high-stakes approach [24]. - Meta is viewed as having a comprehensive strategy with significant potential, while Google is seen as having made a notable turnaround in its AI strategy [25][26].
X @Andy
Andy· 2025-09-29 16:11
RT The Rollup (@therollupco)Avichal shares his mental model to identify protocols that will benefit from institutional adoption."Great tokenomics don't bail you out of the thing not being useful. The 1st question is, is it actually even useful?" https://t.co/eTDzn0SNcG ...
This Penny Stock Is Surging Big Time on a Tie-Up With Faraday Future and a Push Into Crypto. Should You Buy Its Shares Here?
Yahoo Finance· 2025-09-23 14:00
Core Insights - Qualigen Therapeutics (QLGN) shares experienced a significant surge of approximately 200% after securing a $41 million investment from Faraday Future (FFAI) and its founder YT Jia, indicating strong investor enthusiasm for the company's rebranding to CXC10 and its pivot into crypto-Web3 [1][3] - Despite the initial spike, QLGN shares have since retraced much of the gains but remain up nearly 190% from year-to-date lows [2] - The partnership with Faraday, which holds a 55% stake and Jia's additional 7% under a two-year lockup, adds credibility and potential for growth to the newly rebranded CXC10 [3] Financial and Strategic Overview - The pivot towards crypto and Web3 introduces speculative upside, particularly with early gains from the C10 treasury and the promise of AI-driven trading via BesTrade [4] - The transition represents a significant departure from the company's biotech origins, raising concerns about execution and long-term viability [4][5] - Qualigen Therapeutics has a history of operating losses, limited revenue, and a strained balance sheet, which could lead to dilution risks [5][6] Valuation and Market Position - Valuation remains challenging due to the lack of proven product-market fit in the crypto space, with only a 7% gain on its treasury being inconclusive [6] - Regulatory uncertainty, execution risk, and speculative elements contribute to the perception of QLGN stock as a high-risk investment [6]
