Articles

This article offers a detailed introduction to Google's newly released Gemini 3 Flash large model, highlighting its reasoning capabilities, which rival the flagship Gemini 3 Pro model, alongside faster response speeds and substantially lower costs. Achieving 'top-ranking' performance in multiple benchmark tests, including GPQA PhD-level reasoning, multimodal understanding, and coding agents, the Flash version demonstrates a 'compact yet powerful' evolution. The article further explores its core technical principles, such as 'thought levels' parameters and context caching, vital for balancing performance and cost. Gemini 3 Flash shows immense application potential in complex video analysis, mass data extraction, visual question answering, legal and financial services, and software development, garnering widespread praise from the industry and social media. This indicates an acceleration in AI application deployment and widespread adoption.

This article provides an in-depth analysis of OpenAI CEO Sam Altman's core perspectives from a recent interview. He firmly believes OpenAI has not been surpassed by competitors like Google; instead, it is forging a 'system-wide dominance' through a robust closed-loop system comprising models, interfaces, and infrastructure. Altman exhibits disdain for OpenAI's IPO prospects, asserting that maintaining private status is crucial for pursuing 'superintelligence.' He criticizes Google's strategy of awkwardly integrating AI into existing products, predicting a future dominated by new 'AI-first' product paradigms. He discloses OpenAI's strategic pivot towards enterprise-level services, citing the GDP-val benchmark which indicates AI has reached expert proficiency in 74.1% of knowledge work tasks. He further forecasts that humans will transition into 'AI managers,' exploring the profound implications for employment and future societal models. Altman also stresses that trillions in computing power investment will catalyze scientific discoveries and redefines the ambiguous AGI concept as a more formidable 'superintelligence,' signifying AI's enhanced capability to handle complex management roles.

This article features an in-depth interview with Wang Xiaogang, Chairman of DAXIAO Robotics (a spin-off from SenseTime), discussing the current state, challenges, and opportunities in embodied AI. Wang Xiaogang points out that current technical approaches for embodied AI are far from converged, and VLA models exhibit significant flaws, being machine-centric and lacking comprehension of the physical world. DAXIAO Robotics introduces its pioneering ACE technical paradigm, centered on "environment-centric data acquisition + world model + embodied foundation model," emphasizing human-centric data collection and world model construction. The company will focus on integrated hardware-software solutions, aiming for short-term (autonomous navigation for quadruped robots), medium-term (front-end warehouses/flash-sale warehouses), and long-term (home scenarios) goals. It also plans to open-source its world model to establish a key ecological niche in the industry chain. The article further explores the cost of embodied AI, difficulties in data collection, and the industry's competitive landscape, suggesting that leadership positions have not yet been solidified, leaving ample opportunities for startups.

This article deeply analyzes NVIDIA's strategic intent behind its recent open-sourcing of the top-tier AI model Nemotron-3 series and 3 trillion tokens of training data. Against the backdrop of Meta's Llama influence waning and its open-source strategy shifting towards a closed-source approach, NVIDIA's move not only filled a market gap but was also a carefully designed, shrewd business maneuver. The article points out that by lowering the barrier to AI model development, NVIDIA encourages more developers to build and optimize models. Critically, all these models will inevitably rely on NVIDIA's GPU compute to run, thereby forming a business closed loop of 'establishing dominance with software to drive hardware adoption.' The Nemotron-3 series addresses enterprise customization, high token costs, and model transparency issues, strengthening NVIDIA's position as an AI infrastructure provider and rule maker.

This article records the speech delivered by Xu Siqing, founding partner and CEO of Alpha Commune, at the '2025 New Generation AI Entrepreneurship Competition'. His core view is that the development of large language models no longer solely relies on massive computational power but is now driven by algorithmic efficiency. Chinese teams like DeepSeek have achieved breakthroughs through innovations such as Reinforcement Learning (RL) and Mixture of Experts (MoE), not only optimizing resource utilization but also opening up new paths for AI development. Xu Siqing points out that current large models still have limitations in tasks requiring high-precision execution, specifically mentioning that the 'ChatGPT Moment' for Embodied AI has not yet arrived due to the scarcity of training data, and the strategy component is progressing slowly. He calls for exploring more efficient new paradigms based on 'deductive methods' to surpass the limitations of current 'inductive methods', and emphasizes that angel investment requires courage to explore, willingness to take risks, and adherence to first principles. The article also shares Alpha Commune's investment cases in embodied AI data, AI hardware operating systems, and intelligent AI hardware.