近期，Citrini Research 发布的《2028全球智能危机》中，提及人工智能对职场就业的影响。盛大集团创 始人陈天桥向自研AI模型MiroThinker抛出问题：作为AI，你怎么看？如果报告是错的，漏洞在哪？ MiroThinker以第一人称视角，对该报告进行批判性分析。 白领被"重新定义" MiroThinker指出了报告中基于"技术宿命论"的线性思维误区，并提出三重观点，重新厘清AI与人类， 尤其是职场群体的关系定位。 MiroThinker指出，其自身并无独立意图，也不存在取代人类的诉求，本质是人类意志的"放大器"，而 非简单的"替代品"。面对AI对职场带来的影响，其认为AI具有执行任务效率更高、成本更低的特性，要 求对白领社会角色进行重定义，而非岗位消失。 同时，MiroThinker强调决策权的核心价值：AI可以模拟情境、计算现金流，但无法决定谁有资格获取 收益，也无法决定权衡的取舍。这意味着，真正的价值中枢始终握在那个"做出选择"的人手中。 三步转型路径通往2035 MiroThinker还基于AI发展趋势，勾勒出通往2035年的中产阶层三步进阶转型路径，以理性视角揭示未 来职场与社会价 ...

Core Viewpoint - The article discusses the launch of FutureX, the world's first dynamic real-time LLM intelligence future prediction benchmark, which aims to enhance AI's predictive capabilities in uncertain environments, as emphasized by Elon Musk [2][5][4]. Group 1: FutureX Benchmark - FutureX was developed by ByteDance's SEED team in collaboration with Stanford University, Fudan University, and Princeton University, focusing on predicting future events such as stock price movements, sports outcomes, and political election results [5][6]. - The benchmark evaluates AI models based on their ability to analyze current information and make predictions using logical reasoning, trend analysis, and probability calculations, thus enhancing their practical capabilities in complex real-world scenarios [5][6]. Group 2: MiroMind's Performance - MiroMind's model, MiroFlow, achieved first place in the FutureX rankings for two consecutive weeks in September, showcasing its advanced predictive capabilities compared to other international models [8][12]. - MiroMind successfully predicted complex outcomes, such as ATP men's singles rankings and cryptocurrency price movements, demonstrating its robust modeling and risk management abilities [10][11]. Group 3: MiroMind's Predictive Strategy - MiroMind employs a systematic five-step strategy for predictions, which includes detailed planning, data acquisition, understanding rules, dynamic information updates, and probability analysis [13][11]. - The model's core capabilities include information insight, logical reasoning, uncertainty management, and cross-domain integration, allowing it to make informed predictions in various fields [11][13]. Group 4: MiroThinker Model - MiroThinker, MiroMind's flagship foundational model, is designed for reasoning, decision-making, and multi-modal understanding, and is set to be fully open-sourced for global developers and researchers [15][17]. - The model aims to bridge the gap between open-source and closed-source commercial models, enhancing collaboration and innovation in AI development [15][17].

Core Insights - MiroMind aims to build a self-aware digital agent ecosystem, focusing on the continuous evolution of Artificial General Intelligence (AGI) through community collaboration and open-source principles [2][4]. Group 1: Open Source Ecosystem - MiroMind has developed a comprehensive open-source ecosystem that includes the Agent framework (MiroFlow), models (MiroThinker), data (MiroVerse), and training infrastructure (MiroTrain/MiroRL), all of which are open for learning, reuse, and further development [1][8]. - The MiroFlow framework achieved a state-of-the-art (SOTA) score of 82.4 on the GAIA validation set, surpassing existing commercial model APIs [1][12]. - MiroThinker, the core model, reached a SOTA performance of 60.2% on the GAIA-Text-103 dataset, nearing the performance level of OpenAI's Deep Research [1][15]. Group 2: Community Collaboration - MiroMind fosters a developer-centric environment that encourages community participation through data requests, feature customization, and technical challenges, with feedback directly influencing project development [2][22]. - The project organizes various community activities such as competitions, leaderboards, and hackathons to enhance developer engagement and contribution [22]. Group 3: Key Personnel - The project is led by Chen Tianqiao, a renowned entrepreneur known for his strategic vision and significant contributions to brain science and AI [4]. - Dai Jifeng, a key figure in the project, is a professor at Tsinghua University with extensive experience in computer vision and deep learning, having published over 80 papers with significant citations [5][6].