TCW funds, an investment management company, released its “TCW Concentrated Large Cap Growth Fund” third-quarter 2025 investor letter. A copy of the letter can be downloaded here. Equity markets continued their rally in the third quarter, driven by continued optimism around AI investments and positive corporate earnings. Against this backdrop, the fund (I share) returned +4.11% in the quarter compared to +10.51% for the Russell 1000 Growth Index. In addition, please check the fund’s top five holdings to kn ...

Key Points CoreWeave and Applied Digital both had strong performances in 2025. Demand for CoreWeave's AI cloud services is through the roof. Applied Digital, meanwhile, is providing the specialized buildings and access to power for neocloud providers. 10 stocks we like better than Applied Digital › While Applied Digital (NASDAQ: APLD) and CoreWeave (NASDAQ: CRWV) both saw their stocks go on roller-coaster rides in 2025, they were also two of the biggest artificial intelligence (AI) stock winners ...

OpenAI创始人之一，AI大神Andrej Karpathy近日发布年度复盘，称2025年是大型语言模型领域蓬勃发 展的一年，出现了六个关键的"范式转变"拐点。这些变化不仅改变了行业格局，更重要的是揭示了LLM 正在演变成一种全新的智能形态。 12月20日，据硬AI消息，Karpathy在社交平台X上发布的年度复盘中表示，LLM正在演变成一种新型智 能，"比我预期的要聪明得多，同时也比我预期的要笨得多"。 与计算量较小的SFT和RLHF不同，RLVR针对客观且不可作弊的奖励函数，允许更长周期的优化。这种 方法具有极高的"能力/成本比"，吞噬了原本用于预训练的算力。2025年大部分能力提升都源于各实验 室消化这一新阶段的"算力积压"。 他指出，今年出现了6个改变行业格局的"范式转变"关键拐点，其中基于可验证奖励的强化学习 （RLVR）成为LLM生产流程中的新阶段，各大实验室将原本用于预训练的算力转向了更长周期的强化 学习训练。 他特别强调了LLM智能的"锯齿状"特征，称这些模型既是博学的天才，又像是思维混乱的小学生。 Karpathy表示，LLM不是在"进化动物"而是在"召唤幽灵"，这种全新的智能形态需要用不 ...

SUMMARY AND KEY POINTS: Cerence AI will unveil the latest updates to its Cerence xUI™ platform at CES 2026.The company will highlight CaLLM™ Edge running on several different chipsets, delivering faster performance, lower latency, and reliable in-car interaction even without connectivity.Cerence will introduce new AI agents for vehicle owners and dealerships, expanding its presence in the extended automotive ecosystem and beyond. LAS VEGAS and BURLINGTON, Mass., Dec. 18, 2025 (GLOBE NEWSWIRE) -- CES 2026 -- ...

Dubai and Kyiv, December 1, 2025: VEON Ltd. (Nasdaq: VEON), announces that Kyivstar (Nasdaq: KYIV; KYIVW), together with the WINWIN AI Center of Excellence under Ukraine’s Ministry of Digital Transformation, has selected Google Gemma as the base model for developing Ukrainian LLM, Ukraine’s own national large language model (“LLM”), leveraging Google’s Vertex AI infrastructure for computing power. As a strategic partner in this project, Kyivstar will operationally lead the development of Ukrainian LLM. The ...

KYIV, Ukraine, Dec. 01, 2025 (GLOBE NEWSWIRE) -- Kyivstar (Nasdaq: KYIV; KYIVW), Ukraine’s leading digital operator, and the WINWIN AI Center of Excellence under the Ministry of Digital Transformation of Ukraine have selected Google’s Gemma as the base model for training the large language model (LLM). Gemma, Google’s next-generation open AI model, has been proven effective in both international and domestic projects. Kyivstar is the Ukrainian Government’s strategic partner and operational lead for developi ...

Group 1 - The era of scaling is over, and the focus has shifted to research, emphasizing the importance of model generalization over mere computational power [4][8][34] - Emotional value functions are expected to play a crucial role in future AI developments, enhancing the efficiency of reinforcement learning [10][14][18] - The generalization ability of current models is still significantly inferior to that of humans, raising fundamental questions about AI's learning capabilities [13][19][25] Group 2 - The current models exhibit a "zigzag" capability, performing well in evaluations but struggling with real-world applications, indicating a disconnect between training and practical performance [27][30] - Companies that continue to pursue a scaling strategy may generate substantial revenue but could face challenges in achieving profitability due to intense competition [34][35] - The deployment of AI on a large scale could potentially lead to rapid economic growth, although the exact pace of this growth remains uncertain [35] Group 3 - Good research taste is essential, requiring a multi-faceted approach to identify beauty and simplicity in AI development [36][38] - The ultimate goal for AI development should be to create systems that genuinely care for and perceive life, rather than merely focusing on self-evolving AI [39][43] - The timeline for achieving superintelligence is projected to be within the next 5 to 20 years, contingent on advancements in understanding reliable generalization [44][46] Group 4 - SSI's current focus is on research, with plans to gradually deploy AI while ensuring that the first products released are meaningful and impactful [50][56] - SSI differentiates itself through a unique technical approach, aiming to create AI that is aligned with human values and capable of meaningful interaction [58]

Core Insights - The article discusses the ongoing debate in the AI industry regarding the future of large language models (LLMs) and the emergence of reasoning models, highlighting differing opinions among experts [1][4][11]. Group 1: AI Development and Trends - The introduction of reasoning models is seen as a significant breakthrough following the Transformer architecture, which has been influential in AI development since 2017 [3][4]. - Łukasz Kaiser predicts that the next one to two years will see rapid advancements in AI, driven by improvements in GPU and energy resources rather than algorithms [1][17]. - The AI industry is currently engaged in a multi-trillion dollar race towards achieving artificial general intelligence (AGI), with many believing that the combination of LLMs, data, GPUs, and energy will lead to its realization [4][11]. Group 2: Criticism of LLMs - Richard Sutton and Yann LeCun express skepticism about the future of LLMs, suggesting that they have reached a dead end and have not learned from past mistakes [11][13]. - Critics argue that LLMs have inherent limitations in their improvement capabilities, which may be closer than previously thought [13][15]. - François Chollet has initiated the ARC Prize to redirect focus towards more promising paths to AGI, indicating a belief that LLMs are not the right approach [15]. Group 3: Advancements in Reasoning Models - Kaiser counters the notion that LLMs are a dead end, emphasizing that reasoning models require significantly less training data and can accelerate research processes [17][19]. - Reasoning models are capable of self-reflection, dynamic resource allocation, and generating multiple reasoning paths, marking a shift from traditional LLMs [19][23]. - The first reasoning model, o1, has already shown superior performance in reasoning-intensive tasks compared to the strongest general model, GPT-4o [21]. Group 4: Future Directions and Challenges - Kaiser believes that while AI capabilities will continue to grow, there will still be areas where human involvement is irreplaceable, particularly in physical world tasks [27]. - The focus should be on the transformative potential of reasoning models, which can handle specific job tasks effectively and improve overall efficiency [28][30]. - The development of multi-modal training methods is underway, which could significantly enhance AI's understanding of both abstract and physical worlds [40][42].

Core Insights - The rapid advancement of software output speed is significantly influenced by large language models (LLMs) like ChatGPT and GitHub Copilot, which are reshaping the way software developers work [1][2] - While LLMs have increased developer efficiency by 26%, they raise questions about the essence of software development and the potential dilution of creativity and critical thinking [1][2] Research Findings - LLMs enhance developer productivity, maintain development processes, and promote entrepreneurship, but they also pose risks such as damaging developer reputation, fostering laziness, and hindering skill development [2][11] - The research utilized a social technical grounded theory (STGT) approach, involving interviews with 22 software practitioners across three rounds to gather and analyze data [3][5] Usage Statistics - Most participants have used various LLM tools, with ChatGPT being the most frequently used. Approximately 59% of participants interact with LLMs at least six times daily [5][6] Benefits of LLMs - **Individual Level**: LLMs effectively enhance developers' efficiency and learning capabilities by automating code generation, fixing syntax errors, and providing instant feedback, thus helping maintain a "flow" state [7][9] - **Team Level**: LLMs reduce collaboration interference and communication costs, allowing junior developers to resolve issues independently before seeking help from colleagues [9] - **Organizational Level**: LLMs save time and costs for software companies, particularly benefiting small and medium-sized enterprises by enabling them to accomplish more tasks with fewer resources [9] - **Societal Level**: LLMs foster innovation and entrepreneurship by allowing developers to quickly prototype and learn business and technical knowledge, thus lowering the barriers to starting new ventures [9] Drawbacks of LLMs - LLMs can generate erroneous code or suggestions, which may slow down progress and require additional time for validation. Over-reliance on LLMs can weaken developers' code comprehension and motivation to learn [11][13] - Concerns about copyright and licensing issues have led some companies to prohibit the use of LLMs, while the cost of frequent LLM usage can increase operational burdens [13][14] Recommendations for Developers - Developers are encouraged to experiment with different LLMs to find the best fit for their needs, recognizing that LLMs are statistical tools rather than intelligent agents [14][15] - Maintaining a balanced relationship with LLMs is crucial, where developers trust their capabilities while keeping a rational distance to avoid dependency [14][15]

Core Viewpoint - The article discusses the introduction of AdaDrive, an adaptive slow-fast framework for integrating large language models (LLMs) into autonomous driving systems, aiming to balance high reasoning capabilities with real-time performance [2][3][4]. Background Review - Autonomous driving has been a research focus in academia and industry, with the emergence of LLMs enhancing cognitive reasoning and decision-making capabilities in driving systems. Early methods like LMDrive and AD-H faced challenges with memory overhead and latency, particularly in dynamic driving environments [4][7]. AdaDrive Algorithm Overview - AdaDrive is proposed as a next-generation framework that employs a fast-slow system paradigm, balancing high-frequency low-latency tasks with low-frequency high-reasoning tasks. It dynamically determines when to activate LLMs and adjusts their contribution based on scene complexity and prediction confidence [8][10][15]. Key Innovations - The framework introduces two key innovations: adaptive LLM activation, which learns the optimal activation timing through a novel loss function, and dynamic LLM contribution adjustment, which uses confidence-driven strategies to modulate LLM influence [8][9][21]. Experimental Results - AdaDrive demonstrated superior performance in the LangAuto benchmark, achieving driving scores of 80.9% and 70.6% in short-distance tasks, significantly outperforming the second-best method by 12.9% and 16.3% respectively [31][32]. - The method also showed advantages in inference time and memory costs due to its adaptive architecture and custom memory buffer, reducing computational overhead while enhancing driving performance [33]. Conclusion - The research highlights the potential of LLM-based language-guided autonomous driving technology, focusing on optimal activation timing and effective utilization strategies. AdaDrive's adaptive architecture and efficient memory management strategies significantly improve both effectiveness and efficiency compared to existing methods [43].