Core Points - Gorilla Technology Group Inc. announced that holders of Class A contingent value rights (CVRs) will receive a distribution of ordinary shares, subject to the terms outlined in the Contingent Value Rights Agreement [1] - On November 18, 2025, 587,747 Earnout Shares were forfeited by Company Shareholders, which translates to 0.130382275 Ordinary Shares per Qualifying CVR Holder [2] - To become a Qualifying CVR Holder, a holder must submit a Valid Notice containing specific information; otherwise, the notice will be deemed invalid [3][4] Company Overview - Gorilla Technology Group Inc. is headquartered in London, UK, and operates as a global solution provider in Security Intelligence, Network Intelligence, Business Intelligence, and IoT technology [5] - The company offers a variety of solutions across sectors such as Government & Public Services, Manufacturing, Telecom, Retail, Transportation & Logistics, Healthcare, and Education, utilizing AI and Deep Learning Technologies [5] - Gorilla focuses on enhancing urban operations, security, and resilience through AI-driven products, including intelligent video surveillance and advanced cybersecurity technologies [6]

AI Chip Landscape - Tensor Processing Units (TPUs) are purpose-built for AI and deep learning, offering lower total costs and greater power efficiency compared to GPUs [1] - Google has been developing TPUs for some time, aiming for efficiency and supply chain diversification beyond Nvidia [2][3] - Google's full-stack approach, from chip to application, provides significant efficiencies of scale [5][6] - Diversifying chip base is crucial, as different chips excel in different tasks, similar to diversifying cloud providers [10][11] Market Demand and Competition - The AI market is projected to reach a $7 trillion market cap by 2030, indicating substantial demand [8] - The market demand is large enough to accommodate multiple players, suggesting it's not a zero-sum game between CPU and GPU [8][9] - Hyperscalers not directly competing with Google, pharmaceutical giants, energy companies, and governments are potential adopters of TPUs [13][14] - AMD and Google are positioned to provide alternatives to Nvidia's dominance in the AI chip market [15] Google's AI Capabilities - Gemini 3 is competitive with other leading large language models like ChatGPT, Claude, and Perplexity, excelling in various use cases [16][17] - Sovereign AI and companies building data centers/physical AI will drive market headlines in 2026 [24] Nvidia's Outlook - Models suggest Nvidia has the potential for another $1 trillion in sovereign AI market cap and another $1 trillion in physical AI market cap, potentially peaking around $6.5 to $7 trillion market cap [22][23]

Core Insights - Yann LeCun, a prominent figure in AI, is leaving Meta to establish a startup focused on world models that analyze information beyond web data [1][2] - The startup aims to advance AI systems that understand the physical world, possess persistent memory, and can reason and plan complex actions [2] - Meta will collaborate with LeCun's startup, indicating a continued partnership despite his departure [2] Company Developments - Meta's AI unit has undergone significant restructuring following the underwhelming reception of its Llama open-source large language model [3] - CEO Mark Zuckerberg has invested billions to attract top AI talent, including a $14.5 billion investment in Scale AI [3][8] - Recent layoffs at Meta, including 600 employees from the Superintelligence Labs division, have contributed to LeCun's decision to leave [9] Industry Context - LeCun's research has diverged from the current direction of Meta and other tech companies, which focus on foundation models and large language models [6][7] - The need for new computing architectures to achieve artificial general intelligence has been emphasized by LeCun and other deep-learning experts [7] - The competitive landscape in AI is intensifying, with companies like OpenAI and Google adopting different strategies compared to LeCun's open-source advocacy [11]

AI Capabilities & Development - AI is currently understood as neural networks, deep learning (large neural networks), and large language models (big neural networks for autocomplete) [1] - The "bitter lesson" of AI is that scaling up machines with more parameters and data leads to increased intelligence, but whether it can scale to superintelligence remains unknown [1] - The AI industry is in a race to achieve Artificial General Intelligence (AGI), where the winner takes all, incentivizing rapid development and potentially overlooking safety concerns [2][3] Potential Benefits of AI - AI could lead to personalized media, personalized healthcare, and potentially cure all diseases [1] - AI has the potential to eliminate work and usher in an era of play, world peace, and space exploration [1] - AI could significantly improve lives and enhance humanity if aligned with human values [4] Risks & Challenges of AI - AI is distorting reality, making digital verification impossible and leading to the humanization of AI, which can have negative impacts on children [1] - AI could lead to separate realities and erode trust, which is vital for human society [2] - Increased reliance on AI could lead to cybercrime, as AI can be used to generate hacking code, making everyone vulnerable [2] - Uncontrolled superintelligent AI could lead to unintended consequences and potentially the destruction of humanity [2] - Over-reliance on AI could erode human attention, skills, and motivation, leading to premature handover of power to machines [2] AI Alignment & Control - The current approach to AI development, led by entrepreneurs and software developers, prioritizes speed over safety and alignment [4] - AI alignment with humanity must be a core goal, pursued with the same or greater vigor as the pursuit of superintelligence [4] - The industry needs to balance the benefits of AI with the risks and guard against them, advocating for a return to philosophy and exploration of different perspectives [4]

AI Model Advancement - OpenAI's deep learning systems are rapidly advancing, enabling models to solve complex tasks faster [1] - OpenAI is internally tracking towards achieving an intern-level research assistant by September 2026 [1]

Core Insights - Yoshua Bengio is recognized as the most cited computer scientist globally, with a total citation count of 987,920, and has seen a significant increase in citations since winning the Turing Award in 2018 [5][6][29] - Geoffrey Hinton, another prominent figure in AI, is approaching 1 million citations, currently at 972,944, and is expected to become the second individual to surpass this milestone [2][5] - The rise in citations for these AI pioneers reflects the explosive growth of AI research and its integration into various fields, particularly since the introduction of deep learning techniques [14][17][26] Group 1 - Yoshua Bengio's citation metrics include an h-index of 251 and a 110-index of 977, indicating his significant impact in the field of machine learning and deep learning [1][5] - The citation growth for Bengio and Hinton aligns with the overall increase in AI-related publications, which have tripled from 2010 to 2022, highlighting the growing importance of AI in computer science [26][14] - The deep learning community is dominated by a few key figures, with Bengio, Hinton, and Yann LeCun being recognized as the "three giants" of deep learning, all of whom received the Turing Award in 2018 [3][29] Group 2 - The AI research landscape has seen a dramatic increase in the number of papers published, with AI papers constituting 41.8% of all computer science papers by 2023, up from 21.6% in 2013 [26][14] - The introduction of the Transformer model in 2017 and subsequent advancements in generative AI have further accelerated the citation rates of foundational papers in the field [21][23] - The citation counts of leading researchers like Ilya Sutskever and Kaiming He also reflect the growing influence of deep learning, with Sutskever exceeding 700,000 citations and He surpassing 750,000 [34][31]

Core Insights - Yoshua Bengio has become the first scholar globally to surpass one million citations on Google Scholar, marking a significant milestone in AI academic influence [3][5][6] - Geoffrey Hinton follows closely with approximately 970,000 citations, positioning him as the second-highest cited scholar [5][6] - The citation growth of AI papers has surged, reflecting the current AI era's prominence [19][30] Citation Rankings - Yoshua Bengio ranks first globally in total citations, with a significant increase in citations post-2018 when he received the Turing Award [6][9][38] - Geoffrey Hinton ranks second, with a notable citation count of 972,944, showcasing his enduring impact in the field [5][8] - Yann LeCun, another Turing Award winner, has over 430,000 citations, but remains lower than both Bengio and Hinton [13][18] AI Research Growth - The total number of AI papers has nearly tripled from approximately 88,000 in 2010 to over 240,000 in 2022, indicating a massive increase in research output [30] - By 2023, AI papers constituted 41.8% of all computer science papers, up from 21.6% in 2013, highlighting AI's growing dominance in the field [31][32] - The foundational works of AI pioneers have become standard references in subsequent research, contributing to their citation growth [22][33] Key Contributions - The introduction of AlexNet in 2012 is considered a pivotal moment that significantly advanced deep learning methodologies [20] - The development of the Transformer model in 2017 and subsequent innovations like BERT have further accelerated research and citations in AI [24][27] - The increasing number of AI-related submissions to top conferences reflects the field's rapid evolution and the growing interest in AI research [36]

Research & Development Focus - OpenAI is targeting the production of an automated researcher to automate the discovery of new ideas, with a focus on economically relevant advancements [1][3] - The company is extending the reasoning horizon of models, aiming for them to autonomously operate for longer periods, measured by performance in math and programming competitions [3] - OpenAI is working on improving the ability of models to handle more difficult and messy real-world coding environments, focusing on style, proactivity, and latency [12][13] Model Capabilities & Advancements - GPT-5 aims to bring reasoning into the mainstream, improving upon previous models like O3 by delivering reasoning and more agentic behavior by default [1] - The company observed significant progress in models' ability to solve hard science problems, with instances of discovering non-trivial new mathematics [1] - Reinforcement Learning (RL) continues to be a versatile method for continuous improvements, especially when combined with natural language modeling [4][5] Talent & Culture - OpenAI emphasizes fundamental research and innovation, discouraging copying and fostering a culture where researchers are inspired to discover new things [35][36] - The company looks for individuals who have solved hard problems in any field, possessing strong technical fundamentals and the intent to work on ambitious challenges [40] - OpenAI protects fundamental research by delineating researchers focused on algorithmic advances from those focused on product, ensuring space for long-term research questions [46][57] Resource Allocation & Strategy - OpenAI prioritizes core algorithmic advances over product research in compute allocation, but remains flexible to adapt to changing needs [59] - The company believes compute remains a critical resource for advancing AI, not expecting to be data-constrained anytime soon [62][63] - OpenAI acts from a place of strong belief in its long-term research program, not tying it too closely to short-term product reception [70]

Learning Resources - FreeCodeCamp provides resources for learning MCPs from scratch [1] - Deep Learning resources are available [1] - Harvard offers learning materials [1] - Corey provides learning resources [1] - Project-based learning resources are available [1]

Core Insights - Nuix has secured a multiyear contract to provide forensic analysis software to the tax authority of Rhineland-Palatinate, Germany, highlighting its growing influence in regulatory technology [1][4]. Group 1: Contract Details - The contract with the Landesamt für Steuern Rheinland-Pfalz emphasizes Nuix's capability in delivering advanced forensic analysis tools tailored for tax authorities [1][5]. - The selection of Nuix followed a Europe-wide tender process, indicating a competitive evaluation of solutions available in the market [5]. Group 2: Technology and Capabilities - Nuix Neo software automates workflows and can ingest data from over 1,000 file types, utilizing responsible AI and advanced automation to analyze complex datasets [2]. - The software is designed to assist investigators in uncovering financial irregularities and enhancing tax compliance through efficient data analysis [3]. Group 3: Leadership and Vision - Jonathan Rubinsztein, CEO of Nuix, stated that the partnership reflects the trust regulators place in Nuix for complex investigations, reinforcing its position as a leading technology provider in the regulatory space [4]. - The collaboration aims to drive regulatory excellence and innovation, aligning with the shared vision of both Nuix and the Rhineland-Palatinate tax authority [5].