Core Insights - Nvidia is expanding its presence in open source AI through the acquisition of SchedMD and the release of a new model family called Nvidia Nemotron 3 [1][3][6] Group 1: Acquisition of SchedMD - Nvidia has acquired SchedMD, the leading developer of the open source workload management system Slurm, which is essential for high-performance computing and AI [1][2] - The terms of the acquisition were not disclosed, but Nvidia plans to continue operating Slurm as an open source, vendor-neutral software [1][2] Group 2: New Model Release - Nvidia introduced the Nvidia Nemotron 3 family, which is claimed to be the most efficient set of open models for creating accurate AI agents [3][6] - The Nemotron 3 family includes three models: Nemotron 3 Nano for targeted tasks, Nemotron 3 Super for multi-AI agent applications, and Nemotron 3 Ultra for more complex tasks [4] Group 3: Strategic Focus on Open Source - Nvidia's CEO Jensen Huang emphasized that open innovation is crucial for AI progress, aiming to transform advanced AI into an open platform for developers [6] - The company has recently announced additional open source initiatives, including the Alpamayo-R1 model focused on autonomous driving research [7] - Nvidia is positioning itself as a key supplier for robotics and self-driving vehicle companies, betting on physical AI as the next frontier for its GPUs [8]

-These companies investing trillions of dollars in CapEx. -Trillions of dollars in the race to build artificial intelligence support systems. -Trillions of dollars of our tech companies investing in building data centers in America. Westin: How far AI will take us and how fast may depend in part on a basic choice about the overall approach to sharing or withholding information, a choice often mentioned in passing, but one that investors may not have identified as key. -I want to see AI everywhere. You know, ...

Core Insights - The release of China's DeepSeek advanced open-source AI model has intensified competition in the AI sector, particularly against established players like Google and Nvidia [1][3] - Chinese companies are increasingly focusing on open-source AI models, which are accessible and free for developers, leading to more efficient models that require less computing power [2] - Google's Gemini 3, launched on November 16, 2025, aims to compete with OpenAI's GPT-5 and Anthropic's Claude family, enhancing capabilities in coding, searching, and image creation [4] Group 1: Chinese AI Developments - Chinese firms, including Baidu and Alibaba, are pushing the boundaries of open-source AI, with DeepSeek's new model demonstrating strong performance in mathematical reasoning [2][3] - DeepSeek has significantly reduced its API pricing by 63%, making it the lowest globally, which could drive application development and user adoption in China [8] Group 2: Competitive Landscape - Meta Platforms has lost its leading position in open-source AI to Chinese companies, marking a shift in technological competitiveness [3] - Nvidia's stock experienced volatility, dropping 17% after DeepSeek's model release but rebounding to a 30% gain in 2025, highlighting the fluctuating nature of AI stocks [7] Group 3: Google and Market Reactions - Google stock surged by 68% in 2025 as the company effectively integrated AI across various sectors, including search, cloud computing, and digital advertising [6] - Investor concerns regarding Google's core internet search business have been heightened since the introduction of ChatGPT, which provides direct answers to queries [5]

Core Insights - The article discusses the evolving landscape of AI competition leading into 2026, highlighting trends such as the concentration of power among a few key players and the rise of new entrants in the open-source community, particularly from China [3][7][8] - It emphasizes the limitations of current large language models (LLMs) in achieving super intelligence and the challenges in generalization capabilities [15][18][22] - The article also explores the implications of open-source versus closed-source models, talent attraction, and the importance of policy support for fostering innovation in the AI sector [33][40][41] Group 1: AI Competition Trends - The AI industry is witnessing a concentration of power among a few core players due to the availability of computational resources, which will be a significant topic in 2026 [7][11] - There is a notable emergence of new laboratories in China producing high-quality models, which has prompted a resurgence of open-source initiatives in the U.S. as a response to China's advancements [8][9] - Companies seeking to explore new AI applications are increasingly turning to open-source models, as closed-source systems impose limitations [8][10] Group 2: Limitations of Current AI Models - Current LLMs exhibit weaker generalization capabilities than previously expected, leading to a ceiling effect that hinders the achievement of super intelligence [15][18] - The article posits that while AI can serve as a valuable research assistant, it struggles to define new research questions, which is crucial for groundbreaking scientific discoveries [20][22] - The notion that expanding model size will naturally lead to greater intelligence is challenged, with the argument that true innovation requires more than just scaling [22][24] Group 3: Open-source vs Closed-source Dynamics - The choice between open-source and closed-source models is influenced by various factors, including the need to attract top talent and the cultural context of the research environment [36][37] - In the U.S., closed-source models are becoming more attractive for researchers, while in China, open-source models are preferred [37][39] - The article suggests that policy support for open-source initiatives is crucial for maintaining a competitive edge in AI development [40][41] Group 4: Business Model and Future Directions - Hugging Face is transitioning its business model to focus on enterprise solutions, providing tools for organizations to manage and deploy AI models securely [50][51] - The company has entered the robotics field, emphasizing the importance of open-source ecosystems in this domain and launching affordable entry-level robotic products [52][58] - The introduction of a low-cost robotic arm and the Ritchie Mini robot aims to enhance human-robot interaction and make robotics more accessible [58][59]

Core Insights - The article highlights a growing preference for open source AI models in Asia, particularly in China, due to their cost-effectiveness and control over data, contrasting with the U.S. preference for proprietary models [1][2][4] Group 1: Open Source vs Proprietary Models - Open source models are perceived to be more cost-effective and allow companies to maintain control over their data, with examples from companies like SiliconFlow demonstrating significant cost savings [1] - Fine-tuning open source models on proprietary data can lead to better performance than proprietary models, with no risk of data leakage, as emphasized by industry executives [1] - U.S. executives generally prefer proprietary models for their performance advantages and perceived safety, despite a smaller performance gap of 8% in some benchmarks [2][4] Group 2: Regional AI Infrastructure Development - Johor, Malaysia, is positioning itself as a data center hub for Southeast Asia, planning to add 5.8 gigawatts of data center projects, which will consume the state's current electricity generation capacity [6] - Concerns are raised about the impact of data center expansion on local electricity bills and water resources, leading to a pause on new water-cooled facility developments until 2027 [6] Group 3: Geopolitical Dynamics in AI - There is a growing interest among middle-income countries to develop their own AI capabilities to reduce dependence on U.S. and Chinese technologies, as suggested by a white paper from various policy experts [7][8] - The feasibility of forming a non-aligned movement in AI among these countries remains uncertain, but it is a topic of consideration for policymakers [8]

America's Open Source AI Gambit: Two Labs, One Question—Can the US Compete?► https://t.co/2GquG3qCQf https://t.co/2GquG3qCQf ...

Layoff & Personnel Changes - Meta AI laid off 600 employees in October 2025, including the research director of core departments [1] - High-level executives in charge of AI business left or were marginalized [1] - Yann LeCun, a Turing Award winner, was also considered to be in a precarious position [1] AI Strategy & Development - Meta's Llama series was once the pride of the developer community after Yann LeCun joined Meta in 2013 to form FAIR laboratory [1] - After Llama 3's success, Meta's leadership was eager to productize, neglecting FAIR's exploration of cutting-edge technologies like chain of thought [1] - DeepSeek and OpenAI's inference impact led to internal chaos at Meta, temporarily drawing FAIR team to "put out the fire" [1] - Productization pressure led to technical imbalance and project failure [1] - Llama 4 faced a public relations crisis due to cheating rumors and release rhythm issues [1] - Meta AI team was reorganized, with emphasis on "applying AI to products" [1] - Management chaos led to missing the "chain of thought" [1] - 28-year-old Alex Wang was given "unlimited privileges" and reorganized the AI department [1] Open Source Approach - Llama 1 was "accidentally leaked" and established a foundation with a "semi-open source" format [1] - Llama 2 was open and "commercializable", becoming popular in the developer community [1] - The Llama 3 series iterated rapidly, further approaching the closed-source camp [1]

Pinterest CEO Bill Ready says open source AI is offering cost savings to the company, particularly in visual search. https://t.co/SdOHDtheWR ...

Core Insights - Pinterest is focusing on leveraging open-source AI models to reduce costs while expanding its visual AI capabilities [1][5][6] - The company is facing challenges with a predicted weaker holiday shopping season, impacting its fourth-quarter revenue expectations [4] - Pinterest is exploring agentic commerce and enhancing user experience through AI-driven features like Pinterest Assistant [3][10][11] Financial Performance - Pinterest's fourth-quarter revenue is projected to be between $1.31 billion and $1.34 billion, below analysts' expectations of $1.34 billion [4] - The stock price dropped by over 21% following the earnings announcement due to these revenue concerns [4] AI and Technology Utilization - CEO Bill Ready emphasized the effectiveness of open-source models, which have shown significant cost reductions while maintaining comparable performance to proprietary models [6] - The company is actively testing and implementing open-source AI models for various use cases, aiming for cost efficiency [5][6] User Experience and Features - Pinterest is enhancing its shopping experience with AI, including features like "push-button type buying" through partnerships [10] - The introduction of Pinterest Assistant aims to provide personalized recommendations and guidance based on user preferences [11][12]

Almost three years later, how did this play out?Due to an enormous push by e/acc and Elon and the election, the genuinely dystopian threat of centralized woke AI was fought and defeated. The establishment failed to chokepoint the technology on the basis of “safety” concerns.The surprising part is that the Chinese AI and decentralized AI camps ended up overlapping, with the Chinese releasing open weights (albeit not fully open source) models. Since anyone globally can use decentralized models for any purpose ...