RT s4mmy (@S4mmyEth)If Compute + Data = IntelligenceWhat’s stopping China from achieving AGI first?Insane electricity generation to power their GPUs (and in turn drive compute).What data sources are they using to train their models? https://t.co/EBKxRCYz3w ...

Welcome to an AI data center where you can hear >> this is crazy power right now >> and feel >> this can blow dry my hair >> the power >> AI is in there >> by 2028 data centers like this across the country could use 12% of all US electricity enough to power more than 55 million homes for a year and yes you are increasing that energy every time you hit enter on an AI prompt it's routed through places like this. Okay, let's start with tracing this prompt. Create a video of a dancing steak.The moment you hit e ...

Computing Power Evolution - The industry has witnessed a dramatic growth in computing power over the past 5 decades, transitioning from early CPUs to GPUs and now specialized AI processors [4] - GPUs and accelerators have rapidly outpaced traditional CPUs in compute performance, initially driven by gaming [4] - Apple's M4 chip features a neural engine delivering 38 trillion operations per second, establishing it as the most efficient desktop SOC on the market [3] - NVIDIA's B200 delivers 20 quadrillion operations per second at low precision in AI data centers [3] Hardware and AI Development - The development of CUDA by Nvidia in 2006 enabled GPUs to handle more than just graphics, paving the way for deep learning breakthroughs [6] - The "hardware lottery" highlights that progress stems from available technology, not necessarily perfect solutions, as GPUs were adapted for neural networks [7] - As AI scales, general-purpose chips are becoming insufficient, necessitating a rethinking of the entire system [7] Efficiency and Optimization - Quantization is used to reduce the size of numbers in AI, enabling smaller, more power-efficient, and compact AI models [8][10] - Reducing the size of parameters allows for more data movement across the system per second, decreasing bottlenecks in memory and network interconnects [10][11] - Wafer Scale Engine 2 achieves similar compute performance to 200 A100 GPUs while using significantly less power (25kW vs 160kW) [12] Future Trends - Photonic computing, using light instead of electrons, promises faster data transfer, higher bandwidth, and lower energy use, which is key for AI [15] - Thermodynamic computing harnesses physical randomness for generative models, offering efficiency in creating images, audio, and molecules [16] - AI supercomputers, composed of thousands or millions of chips, are essential for breakthroughs, requiring fault tolerance and dynamic rerouting capabilities [17][20] Global Collaboration - Over a third of all US AI research involves international collaborators, highlighting the importance of global connectedness for progress [22] - The AI supply chain is complex, spanning multiple continents and involving intricate manufacturing processes [22]

Your investments also include Uber, Coinbase, but the air space, we think a perplexity. We also think of AI and let's just talk about that because it's recently been purchased by Metta which is on the hunt for deals, is on the hunt to lead in superintelligence. We could get more of that, Steve.Sure. Yeah, Good question. And thanks for having me on.You know, I think what's happening right now is, you know, his case is that they're looking to completely overhaul their their air research team and also their re ...

Summary of Key Points from the Conference Call Industry Overview - The focus of the conference call is on the AI infrastructure and data center industry, particularly the financing needs and power bottlenecks associated with AI adoption and data center expansion [1][3][35]. Core Insights and Arguments - **Global Data Center Spending**: An estimated $2.9 trillion will be spent on global data centers through 2028, with 85% allocated for AI-specific data centers [4][38]. - **Financing Gap**: There is a projected $1.5 trillion gap in data center investment that will require external financing, particularly as hyperscalers slow down their capital expenditures [8][16]. - **Private Credit Opportunities**: The private credit market is expected to present an $800 billion opportunity to finance data center capital expenditures from 2025 to 2028 [10][30]. - **Securitization Growth**: The rate of securitization in credit markets is anticipated to increase from 10% to 25% by 2028, providing competitive financing costs for developers [24][28]. - **Hyperscaler Cash Flow**: Hyperscalers are expected to fund approximately $1.4 trillion of their capital expenditures through cash flows, but shareholder returns and acquisitions may limit practical spending on AI [16][19]. - **Corporate Debt Issuance**: A forecast of $200 billion in corporate debt issuance is expected, with hyperscalers capable of issuing over $500 billion without impacting their credit ratings [19][21]. Risks and Challenges - **Credit Market Dynamics**: Positive real yields have attracted long-term buyers, but high funding costs and macroeconomic uncertainty may pose risks to financing capacity [15][14]. - **Power Bottlenecks**: The U.S. and Europe face multiple bottlenecks in data center growth, including grid access, power equipment, labor, and political capital [50][52]. - **Grid Instability**: Recent events have raised concerns about grid stability, which could impact data center operations [68][75]. AI Adoption and Market Trends - **Non-Linear AI Improvement**: The rate of AI capability improvement is expected to be non-linear, with significant advancements predicted in the coming years [36][64]. - **AI-Driven Revenue Opportunities**: The generative AI sector is projected to create a revenue opportunity of approximately $1 trillion by 2028, with substantial growth in software and consumer spending [44][46]. - **Sectoral Exposure to AI**: A broadening of AI exposure is noted across various sectors, with significant increases in materiality among companies in consumer durables, real estate, and financial services [73][74]. Additional Insights - **GPU Financing**: There is skepticism regarding the ability of non-investment grade companies to finance GPU purchases, suggesting that loans backed by GPUs may become a popular solution [33]. - **Potential AI Technology Restrictions**: There is a possibility of increased restrictions on AI technology exports to China, which could impact global competition in AI development [71]. - **Investment Strategies**: Suggested investment strategies include overweighting stocks with increased AI exposure and materiality, focusing on companies with strong pricing power and those central to AI proliferation [74]. This summary encapsulates the key points discussed in the conference call, highlighting the significant trends, challenges, and opportunities within the AI infrastructure and data center industry.

RT Danny Limanseta (@DannyLimanseta)Gaming laid a lot of the foundational tech for the AI advancements today.@demishassabis had a great quote during @lexfridman's pod where he talks about this.“In the 90s, all of the most interesting technical advancement were happening in gaming, whether it is AI, graphics, physics engines, hardwares like GPUs were designed for gaming initially.Everything that was pushing computer forward in the 90s was due to gaming. Interestingly, that was where the forefront of research ...

Intel Corporation (NASDAQ: INTC ) designs and manufactures semiconductor products. Wow, you might say, this is the spot to be. You will probably recall quickly that nowadays, whatever is linked with CPUs, GPUs, and chips in general enjoys the narrative of producing key items to allow the AII’m a long-term growth and dividend-growth investor covering both US and European equity markets. I seek undervalued stocks and high-quality dividend growers that generate dependable cash flow for reinvestment. I share on ...

RT Tetsuo (@tetsuoai)Grok 4 Video + Voice is able to identify GPUs by looking at the chipset. Pretty impressive. https://t.co/DK3ml02XZC ...

It is time. It's time for the light round for your stock by also tip no stock by step of graphics play and then the lightning round is over. Are you ready to the light round with Dan in South Carolina Dan.>> How you doing. Dan from South Carolina. I got a question.How you doing Kramer. >> Sure. I'm doing all right.How about you partner. Good. Good.COP, I've been wondering. >> All right. Bye, COP.Let me tell you, I just told Jeff Marx, my partner uh for the club, that we're in the wrong one now. Cotara's not ...

Core Viewpoint - Nvidia experienced a significant market value decline earlier in 2025, dropping to approximately $2.3 trillion, a 37% decrease from its peak, but has since rebounded to over $4 trillion following a strong earnings report in May [1][4]. Group 1: Factors Influencing Market Performance - The emergence of a Chinese start-up, DeepSeek, raised concerns among investors about the necessity of Nvidia's high-priced chipsets for AI infrastructure [2]. - New U.S. tariff policies and rising competition from Advanced Micro Devices, along with investments in custom silicon by major cloud providers, contributed to fears regarding Nvidia's growth prospects [3]. Group 2: Valuation Trends - Nvidia's current price-to-sales (P/S) and forward price-to-earnings (P/E) ratios are significantly lower than the highs seen during the AI revolution, indicating potential for future growth despite current multiples being down [5][7][8]. Group 3: Growth Catalysts - Nvidia's growth has primarily stemmed from its data center operations, but there are additional opportunities in sectors like autonomous driving, where it generated $567 million in sales from automotive services, reflecting a 72% year-over-year growth [9][10]. - The company is also involved in AI-powered robotics and quantum computing, with investments in companies like Figure AI and the development of the CUDA-Q platform for quantum applications [12][13]. Group 4: Investment Outlook - The technologies of autonomous driving, robotics, and quantum computing are still in nascent stages, presenting significant disruption potential across various industries, positioning Nvidia favorably to capitalize on these trends [15]. - Despite concerns about maturing operations and growth acceleration, the company's resilience and numerous growth opportunities suggest a positive long-term outlook for Nvidia stock [16][17].