对于大模型初创企业，一些商业模式是不健康的 2025年上半年，大模型的行业应用爆发式增长。 DeepSeek在全国数以十万计的企业层面，起到了AI技术"扫盲"的作用。但在商业化层面，阿里巴巴、阶跃星辰以及百度等企业先行一 步，衔接起上下游合作方，率先跑通了业务流程。 开源在中国已成风潮，阿里巴巴、百度等企业都有开源模型。但在大模型落地具体行业和公司的时候，开源和闭源之争仍在继续。 阶跃星辰一直走的是超级模型和超级应用并重的路线。基础模型强调通用的能力，像尚未选择专业的本科生，应用则相当于本科生已进 行专业训练。阶跃星辰两方面都不愿意舍弃，因为模型能力决定应用的上限，而应用也给模型提供具体的场景和数据反馈。 阶跃星辰的主要收入来源，也主要是企业端，而非普通消费者。手机产业目前是它最活跃的落地场景，国内头部手机企业中一半都有合 作。此外，它的合作方还包括了一些汽车企业，比如和吉利共同打造智能座舱等。 2025年的春节，DeepSeek大模型发布。尽管OpenAI率先撬动了大模型技术，但在中国企业界完成AI技术"扫盲"的是DeepSeek，它在数以 十万计的中国企业间，迅速完成了一次知识普及。DeepSeek短期内 ...

Core Insights - The future of technology in the next 5 to 10 years will be dominated by generative AI, which is considered a significant leap from ChatBot to Agent [3][4] - The competition between the US and China in AI is not about which company is stronger, but rather a contest between open-source and closed-source approaches [5][16] Investment Opportunities - Nvidia remains a solid investment choice, but investors should look for the right entry points [6][19] - Among the US tech giants, Microsoft is favored due to its willingness to invest boldly and its clear understanding of profitable business models [22] AI Development Trends - The era of AI 2.0, driven by generative AI, is expected to create substantial economic value across various industries [8] - The scaling law for pre-training has reached its limits, while the scaling law for inference is emerging as a new paradigm for model intelligence growth [9][10] - China's open-source model development is catching up to the US, with significant contributions from companies like Alibaba and DeepSeek [13][17] Competitive Landscape - The US has strong payment capabilities from both enterprises and consumers, which China has yet to match [14] - The key competition between the US and China lies in the open-source versus closed-source model, with China currently favoring the open-source route [15][16]

Core Viewpoint - The current large language models (LLMs) are limited in their ability to generate original scientific discoveries and truly understand the complexities of the physical world, primarily functioning as advanced pattern-matching systems rather than exhibiting genuine intelligence [1][3][4]. Group 1: Limitations of Current AI Models - Relying solely on memorizing vast amounts of text is insufficient for fostering true intelligence, as current AI architectures struggle with abstract thinking, reasoning, and planning, which are essential for scientific discovery [3][5]. - LLMs excel at information retrieval but are not adept at solving new problems or generating innovative solutions, highlighting their inability to ask the right questions [6][19]. - The expectation that merely scaling up language models will lead to human-level AI is fundamentally flawed, with no significant advancements anticipated in the near future [19][11]. Group 2: The Need for New Paradigms - There is a pressing need for new AI architectures that prioritize search capabilities and the ability to plan actions to achieve specific goals, rather than relying on existing data [14][29]. - The current investment landscape is heavily focused on LLMs, but the diminishing returns from these models suggest a potential misalignment with future AI advancements [18][19]. - The development of systems that can learn from natural sensors, such as video, rather than just text, is crucial for achieving a deeper understanding of the physical world [29][37]. Group 3: Future Directions in AI Research - The exploration of non-generative architectures, such as Joint Embedding Predictive Architecture (JEPA), is seen as a promising avenue for enabling machines to abstractly represent and understand real-world phenomena [44][46]. - The ability to learn from visual and tactile experiences, akin to human learning, is essential for creating AI systems that can reason and plan effectively [37][38]. - Collaborative efforts across the global research community will be necessary to develop these advanced AI systems, as no single entity is likely to discover a "magic bullet" solution [30][39].