1月26日，国产AI大模型公司阶跃星辰（StepFun）宣布完成超50亿人民币+轮融资，一举刷新过去12个月中国大模型赛道单笔最高融资纪录。 该轮融资参与机构包括：上国投先导基金、国寿股权、浦东创投、徐汇资本、无锡梁溪基金、厦门国贸、华勤技术等产业投资人，腾讯、启明、五源等老 股东进一步跟投。 目前阶跃星辰已发布3代基础大模型，其中Step 3推理效率创行业新高，公司也同时发力全模态（语音、图像等）和端云结合两大方向。2025年12月，阶 跃发布了行业内首款可部署GUI开源模型，以端云结合方案支持手机、车、电脑多端部署。2026年1月，阶跃语音模型Step Audio R1.1在Artificial Analysis 权威榜单登顶。 据内部人士透露，国内60%的头部手机品牌已与阶跃达成深度合作，模型装机量超过4200万台，日均服务近2000万人次；同时，阶跃星辰与千里科技、吉 利共同推出的搭载端到端语音模型的AgentOS智能座舱，量产车型吉利银河M9上市3个月销量接近4万辆，并已进军海外市场，预计今年阶跃大模型将"上 车"超百万辆。 阶跃星辰CEO姜大昕为前微软全球副总裁，是自然语言处理领域专家，在机器学习 ...

Core Insights - Jiyue Xingchen has successfully completed a B+ round financing exceeding 5 billion RMB, setting a record for the largest single financing in China's large model sector in the past 12 months [1] - The company, founded in Shanghai in 2023, has released over 30 large models and is recognized as a leader in multimodal AI [1] - The appointment of AI leader Yin Qi as chairman is expected to enhance the company's competitive edge in the large model market [1] Financing and Investment Landscape - The financing round is notable as it includes a diverse range of investors, such as state-owned funds, industry capital, and strategic investment institutions, indicating a shift in investment patterns in the AI sector [3][4] - In 2025, the total disclosed investment in AI model companies was 9.416 billion RMB, showing a decline compared to 2024, with only three companies securing over 1 billion RMB in single-round financing [2] - The trend indicates a "Matthew Effect" where funding is increasingly concentrated among leading firms, with Jiyue Xingchen's financing being a rare exception in a cooling investment environment [2] Company Strategy and Team - Jiyue Xingchen's core team, led by Yin Qi and other top talents, combines strategic leadership with advanced technical expertise, positioning the company for success in the competitive landscape [5][7] - The company emphasizes the importance of self-sustaining business models, as evidenced by the successful IPOs of peers like MiniMax and Zhipu AI [3] - The strategic focus on "AI + terminal" aims to create a unique synergy between AI models and physical devices, enhancing market penetration and user engagement [15][17] Technological Advancements - Jiyue Xingchen has developed a comprehensive base model matrix, including the Step 3 model, which is capable of complex reasoning and multimodal interactions [9][11] - The company is recognized for its significant investment in AI infrastructure, which is crucial for the development and deployment of AI applications [8] - The integration of AI with physical devices, such as smartphones and vehicles, is expected to create new business opportunities and enhance user experiences [15][17] Market Position and Future Outlook - Jiyue Xingchen has established partnerships with major smartphone brands, achieving significant model installation and user engagement metrics [17] - The company is poised to capitalize on the emerging "physical AI" trend, which emphasizes the integration of AI with real-world applications and interactions [14][15] - As the AI industry evolves, Jiyue Xingchen's focus on innovative commercial pathways and technology-driven solutions positions it well for future growth [13][18]

Core Insights - StepFun has completed a B+ round financing exceeding 5 billion yuan, with investors including various industry funds and existing shareholders like Tencent and Qiming [2] - The financing will be used for foundational model research and to accelerate the implementation of the AI+ terminal strategy [3] Group 1: Leadership and Management - Yin Qi has been appointed as the chairman of StepFun, responsible for overall strategic direction and technology [3] - Yin Qi has extensive experience in the AI field and is a first-generation AI entrepreneur, previously founding Megvii Technology [3] Group 2: Company Strategy and Development - StepFun aims to develop a top-tier foundational model and has already released three generations of foundational models, with Step 3 achieving industry-leading inference efficiency [4] - The company is focusing on multi-modal models and cloud-edge collaboration, targeting applications in automotive, mobile, and wearable devices [4] Group 3: Market Position and Collaborations - StepFun has established deep collaborations with 60% of leading domestic smartphone brands, with model installations exceeding 42 million units and daily services reaching nearly 20 million users [4] - The company has partnered with Qianli Technology and Geely to launch the AgentOS smart cockpit, with the Geely Galaxy M9 model achieving nearly 40,000 sales within three months of its launch [4]

两年多长成一只超级独角兽，阶跃星辰成长速度惊人；而在过去一个月，智谱AI、Mi n iMa x排队上市，双双缔造千亿市值，我们正 在亲历着中国波澜壮阔的AI时代。 分水岭。 作者/刘博 AI圈依旧震撼。 投资界获悉，阶跃星辰正式 完成超5 0亿人民币B+轮融资， 一举刷新过去1 2个月中国大模型赛道单笔最高融资纪录 。参与机构包括 上国投先导基金、国寿股权、浦东创投、徐汇资本、无锡梁溪基金、厦门国贸、华勤技术等产业投资人，腾讯、启明、五源等老股东 进一步跟投。 外界可能不知道，2 0 2 3年阶跃星辰成立于上海，至今已发布超3 0款大模型，被外界视为"多模态卷王"。同样在今天， 公司迎来一位 AI领军人物挂帅——印奇 ，现千里科技董事长 ，正式出任阶跃星辰董事长。他将带领阶跃星辰打响大模型突围赛的第一枪。 创 投 圈 一 个 普 遍 观 点 是 ， 随 着 过 去 一 年 De e p S e e k 迅 速 爆 红 ， 投 资 人 意 识 到 基 础 大 模 型 尚 未 到 达 天 花 板 上 限 ， 赛 道 开 始 进 入 慢 性 淘 汰，这时候需要的是押注优质标的。 那么，何为优质标的？眼下投资人最看重 ...

Core Viewpoint - The article discusses the rapid advancements in multimodal AI models, particularly focusing on Alibaba's Qwen series and the competitive landscape among various domestic companies in China, highlighting the shift from single-language models to multimodal integration as a pathway to achieving Artificial General Intelligence (AGI) [1][3][7]. Group 1: Multimodal AI Developments - Alibaba's Qwen-Image-Edit, based on the 20B parameter Qwen-Image model, enhances semantic and visual editing capabilities, supporting bilingual text modification and style transfer [1][4]. - The global multimodal AI market is projected to reach $2.4 billion by 2025 and $98.9 billion by the end of 2037, indicating significant growth potential in this sector [1][3]. - Major companies, including Alibaba, are intensifying their focus on multimodal capabilities, with Alibaba's Qwen2.5 series demonstrating superior visual understanding compared to competitors like GPT-4o and Claude3.5 [3][5]. Group 2: Competitive Landscape - Other domestic firms, such as Step and SenseTime, are also launching new multimodal models, with Step's latest model supporting multimodal reasoning and complex inference capabilities [5][6]. - The rapid release of various multimodal models by companies like Kunlun Wanwei and Zhiyuan reflects a strategic push to capture developer interest and establish influence in the multimodal domain [5][6]. - The competition in the multimodal space is still in its early stages, providing opportunities for companies to innovate and differentiate their offerings [6][9]. Group 3: Challenges and Future Directions - Despite advancements, the multimodal field faces significant challenges, including the complexity of visual data representation and the need for effective cross-modal mapping [7][8]. - Current multimodal models primarily rely on logical reasoning, lacking strong spatial perception abilities, which poses a barrier to achieving true AGI [9]. - The industry is expected to explore how to convert multimodal capabilities into practical productivity and social value as technology matures [9].

Core Insights - The article highlights the rapid advancements in multimodal AI models, particularly by companies like Alibaba, which has launched several models in a short span, indicating a shift from single-language models to multimodal integration as a pathway to AGI [1][2][6] - The global multimodal AI market is projected to grow significantly, reaching $2.4 billion by 2025 and an astonishing $98.9 billion by the end of 2037, showcasing the increasing importance of multimodal capabilities in AI applications [1][6] Company Developments - Alibaba has introduced multiple multimodal models, including Qwen-Image-Edit, which enhances image editing capabilities by allowing semantic and appearance modifications, thus lowering the barriers for professional content creation [1][3] - The Qwen2.5 series from Alibaba has shown superior visual understanding capabilities compared to competitors like GPT-4o and Claude3.5, indicating a strong competitive edge in the market [3] - Other companies, such as Step and SenseTime, are also making significant strides in multimodal AI, with new models that support multimodal reasoning and improved interaction capabilities [4][5] Industry Trends - The industry is witnessing a collective rise of Chinese tech companies in the multimodal space, challenging the long-standing dominance of Western giants like OpenAI and Google [6][7] - The rapid iteration of models and the push for open-source solutions are strategies employed by various firms to capture developer interest and establish influence in the multimodal domain [5][6] - Despite the advancements, the multimodal field is still in its early stages, facing challenges such as the complexity of visual data representation and the need for effective cross-modal mapping [6][7] Future Outlook - The year 2025 is anticipated to be a pivotal moment for AI commercialization, with multimodal technology driving this trend across various applications, including digital human broadcasting and medical diagnostics [6][8] - The industry must focus on transforming multimodal capabilities into practical productivity and social value, which will be crucial for future developments [8]

Core Insights - The article highlights the rapid advancements in multimodal AI models, particularly by companies like Alibaba, which has launched several models in a short span, indicating a shift from single-language models to multimodal integration as a pathway to AGI [1][2][3] Industry Developments - Alibaba's Qwen-Image-Edit, based on a 20 billion parameter model, enhances semantic and appearance editing capabilities, supporting bilingual text modification and style transfer, thus expanding the application of generative AI in professional content creation [1][3] - The global multimodal AI market is projected to grow significantly, reaching $2.4 billion by 2025 and an astonishing $98.9 billion by the end of 2037, indicating strong future demand [1] - Major companies are intensifying their focus on multimodal capabilities, with Alibaba's Qwen2.5 series demonstrating superior visual understanding compared to competitors like GPT-4o and Claude3.5 [3][4] Competitive Landscape - Other companies, such as Stepwise Star and SenseTime, are also making strides in multimodal AI, with Stepwise Star's new model supporting multimodal reasoning and SenseTime's models enhancing interaction capabilities [4][5] - The rapid release of multiple multimodal models by various firms aims to establish a strong presence in the developer community and enhance their influence in the multimodal space [5] Technical Challenges - Despite the advancements, the multimodal field is still in its early stages compared to text-based models, facing significant challenges in representation complexity and semantic alignment between visual and textual data [8][10] - Current multimodal models primarily rely on logical reasoning, lacking strong spatial perception abilities, which poses a barrier to achieving embodied intelligence [10]

Group 1 - The core concept of AI Infrastructure (AI Infra) encompasses both hardware and software components [2][3] - Hardware includes AI chips, GPUs, and switches, while the software layer can be likened to cloud computing, divided into three layers: IaaS, PaaS, and an optimization layer for training and inference frameworks [3][4][5] - The rise of large models has created significant opportunities for AI Infra professionals, marking a pivotal moment similar to the early days of search engines [8][12] Group 2 - AI Infra professionals are increasingly recognized as essential to the success of AI models, with their role evolving from support to a core component of model capabilities [102][106] - The performance of AI models is heavily influenced by the efficiency of the underlying infrastructure, with metrics such as model response latency and GPU utilization being critical [19][40] - Companies must evaluate the cost-effectiveness of building their own infrastructure versus utilizing cloud services, as optimizing infrastructure can lead to substantial savings [22][24] Group 3 - The distinction between traditional infrastructure and AI Infra lies in their specific hardware and network requirements, with AI Infra primarily relying on GPUs [14][15] - Future AI Infra professionals will likely emerge from both new engineers and those transitioning from traditional infrastructure roles, emphasizing the importance of accumulated knowledge [16][18] - The collaboration between algorithm developers and infrastructure engineers is crucial, as both parties must work together to optimize model performance and efficiency [56][63] Group 4 - The emergence of third-party companies in the AI Infra space is driven by the need for diverse API offerings, although their long-term viability depends on unique value propositions [26][29] - Open-source models can stimulate advancements in AI Infra by encouraging optimization efforts, but excessive focus on popular models may hinder innovation [84][87] - The integration of domestic chips into AI Infra solutions is a growing area of interest, with efforts to enhance their competitiveness through tailored model designs [85][97]

Core Viewpoint - The rise of large models has created significant opportunities for AI infrastructure (AI Infra) professionals, marking a pivotal moment for the industry [7][10][78]. Group 1: Understanding AI Infra - AI Infra encompasses both hardware and software components, with hardware including AI chips, GPUs, and switches, while software can be categorized into three layers: IaaS, PaaS, and an optimization layer for training and inference frameworks [3][4][5]. - The current demand for AI Infra is driven by the unprecedented requirements for computing power and data processing brought about by large models, similar to the early days of search engines [10][11]. Group 2: Talent and Industry Dynamics - The industry is witnessing a shift where both new engineers and traditional Infra professionals are needed, as the field emphasizes accumulated knowledge and experience [14]. - The success of AI Infra professionals is increasingly recognized, as they play a crucial role in optimizing model performance and reducing costs [78][81]. Group 3: Performance Metrics and Optimization - Key performance indicators for AI Infra include model response latency, data processing efficiency per GPU, and overall cost reduction [15][36]. - The optimization of AI Infra can lead to significant cost savings, as demonstrated by the example of improving GPU utilization [18][19]. Group 4: Market Opportunities and Challenges - Third-party companies can provide value by offering API marketplaces, but they must differentiate themselves to avoid being overshadowed by cloud providers and model companies [22][24]. - The integration of hardware and model development is essential for creating competitive advantages in the AI Infra space [25][30]. Group 5: Future Trends and Innovations - The future of AI models may see breakthroughs in multi-modal capabilities, with the potential for significant cost reductions in model training and inference [63][77]. - Open-source models are expected to drive advancements in AI Infra, although there is a risk of stifling innovation if too much focus is placed on optimizing existing models [69][70]. Group 6: Recommendations for Professionals - Professionals in AI Infra should aim to closely align with either model development or hardware design to maximize their impact and opportunities in the industry [82].

Core Insights - The article discusses the rapid advancements in the AI large model industry, highlighting the emergence of "embodied intelligence" as a significant trend, with major companies showcasing their latest technologies at the World Artificial Intelligence Conference (WAIC) [15][16][27]. Group 1: Industry Trends - The WAIC attracted over 350,000 attendees and featured more than 800 exhibitors, showcasing over 3,000 cutting-edge technologies, indicating a strong interest in AI applications and industry collaboration [15]. - The trend of "embodied intelligence" is shifting AI from virtual environments to physical applications, such as robots and smart devices, enhancing real-world interactions [15][16]. - The development of multi-agent systems is becoming prominent, allowing multiple AI agents to collaborate on complex tasks, improving efficiency and aligning with real-world operational logic [17][18]. Group 2: Major Company Developments - Alibaba launched several models at WAIC, including the Qwen3 series, which outperformed closed-source models in various evaluations, emphasizing its commitment to open-source AI [21][22]. - ByteDance introduced new models like Doubao 3.0 for image editing and a simultaneous interpretation model, showcasing its diverse AI capabilities across different domains [23][24]. - Huawei unveiled the Ascend 384 super node, achieving 300 PFLOPS computing power, significantly enhancing the performance of large models [26][27]. Group 3: Open Source Initiatives - The open-source movement in the AI sector is gaining momentum, with major companies like Alibaba and ByteDance releasing models to foster innovation and collaboration within the developer community [19][20]. - The open-source models are expected to accelerate application development and attract more talent and resources into the ecosystem, marking a new phase in the domestic AI landscape [20]. Group 4: Performance Metrics - The GLM-4.5 model from Zhiyuan AI achieved a significant reduction in inference costs while maintaining high performance across various benchmarks, indicating advancements in model efficiency [40]. - The Kimi K2 model from Moonlight achieved a high performance rating in mathematical reasoning and multi-language support, setting a new standard for open-source models [47][48].