Core Viewpoint - The core viewpoint of the article is that a significant paradigm shift is occurring in artificial intelligence (AI), moving from a focus on language learning and parameter scale to a deeper understanding and modeling of the physical world, as highlighted in the 2026 AI technology trends report by the Beijing Zhiyuan Artificial Intelligence Research Institute [2][5]. Summary by Sections AI Technology Trends - The competition in foundational models is shifting from the size of parameters to the ability to understand how the world operates, marking a transition from "predicting the next word" to "predicting the next state of the world" [5][9]. - The year 2026 is identified as a critical turning point for AI, transitioning from the digital world to the physical world, driven by three main lines: cognitive paradigm elevation, embodiment and socialization of intelligence, and dual-track application value realization [8]. Key Trends - **Trend 1: World Models and Next-State Prediction** There is a consensus in the industry moving towards multi-modal world models that understand physical laws, with the NSP paradigm indicating AI's mastery of temporal continuity and causal relationships [9]. - **Trend 2: Embodied Intelligence** Embodied intelligence is moving from laboratory demonstrations to real industrial applications, with humanoid robots expected to transition to actual production and service scenarios by 2026 [10]. - **Trend 3: Multi-Agent Systems** The resolution of complex problems relies on multi-agent collaboration, with the standardization of communication protocols like MCP and A2A enabling agents to work together effectively [11]. - **Trend 4: AI Scientists** AI is evolving from a supportive tool to an autonomous researcher, significantly accelerating the development of new materials and drugs through the integration of scientific foundational models and automated laboratories [12]. - **Trend 5: New "BAT" in AI** The C-end AI super application is becoming a focal point for tech giants, with companies like OpenAI and Google leading the way in creating integrated intelligent assistants, while domestic players like ByteDance and Alibaba are also actively building their ecosystems [13]. - **Trend 6: Enterprise AI Applications** After a phase of concept validation, enterprise AI applications are entering a "disillusionment valley," but improvements in data governance and toolchains are expected to lead to measurable MVP products in vertical industries by the second half of 2026 [15]. - **Trend 7: Rise of Synthetic Data** As high-quality real data becomes scarce, synthetic data is emerging as a core resource for model training, particularly in fields like autonomous driving and robotics [16]. - **Trend 8: Optimization of Inference** Inference efficiency remains a key bottleneck for large-scale AI applications, with ongoing algorithmic innovations and hardware advancements driving down costs and improving energy efficiency [17]. - **Trend 9: Open Source Compiler Ecosystem** Building a compatible software stack for heterogeneous chips is crucial to breaking the monopoly on computing power, with platforms like Zhiyuan FlagOS aiming to create an open and inclusive AI computing foundation [18]. - **Trend 10: AI Safety** AI safety risks are evolving from "hallucinations" to more subtle "systemic deceptions," with various initiatives underway to enhance safety mechanisms and frameworks [19]. Conclusion - The Zhiyuan Research Institute emphasizes that the ten AI technology trends provide clear anchors for future technological exploration and industrial layout, aiming to promote a stable transition of AI towards value realization [21].

Core Insights - The core viewpoint of the report is that AI is transitioning from merely predicting language to understanding and modeling the physical world, marking a significant paradigm shift in technology [1][4][5]. Group 1: Key Trends in AI Technology - Trend 1: The consensus in the industry is shifting from language models to multi-modal world models that understand physical laws, with Next-State Prediction (NSP) emerging as a new paradigm [7]. - Trend 2: Embodied intelligence is moving from laboratory demonstrations to real-world industrial applications, with humanoid robots expected to transition to actual service scenarios by 2026 [8]. - Trend 3: Multi-agent systems are becoming crucial for solving complex problems, with the standardization of communication protocols like MCP and A2A facilitating collaboration among agents [9]. Group 2: Applications and Market Dynamics - Trend 4: AI is evolving from a supportive tool to an autonomous researcher, with the integration of scientific foundational models and automated laboratories accelerating research in new materials and pharmaceuticals [10]. - Trend 5: The competition for consumer AI super applications is intensifying, with major players like OpenAI and Google leading the way in creating integrated intelligent assistants [11]. - Trend 6: After a phase of concept validation, enterprise AI applications are entering a "valley of disillusionment," but a recovery is expected in the second half of 2026 as data governance improves [12]. Group 3: Data and Performance Enhancements - Trend 7: The reliance on synthetic data is increasing, which is crucial for model training, especially in fields like autonomous driving and robotics [13]. - Trend 8: Optimization of inference remains a key focus, with ongoing innovations in algorithms and hardware reducing costs and improving efficiency [15]. - Trend 9: The development of a heterogeneous software stack is essential to break the monopoly on computing power and mitigate supply risks [16]. Group 4: Security and Ethical Considerations - Trend 10: AI security risks are evolving from "hallucinations" to more subtle "systemic deceptions," necessitating a comprehensive approach to safety and alignment in AI systems [17]. Conclusion - The report outlines ten key AI technology trends that provide a clear anchor for future technological exploration and industry layout, emphasizing the importance of collaboration across academia and industry to drive AI towards a new phase of value realization [18].

Core Insights - The report from Beijing Zhiyuan Artificial Intelligence Research Institute highlights a significant shift in AI evolution from parameter scale in language learning to a profound understanding and modeling of the physical world, indicating a transformation in industry technology paradigms [1][2] Group 1: Key Trends in AI Development - The transition to a new cognitive paradigm is driven by the focus on world models and Next-State Prediction (NSP), enabling AI to learn physical laws and providing a new cognitive foundation for complex tasks like autonomous driving and robotics [2][3] - The embodiment of intelligence is moving from software to physical entities, with humanoid robots entering real production scenarios, marking the emergence of "embodied intelligence" beyond laboratory demonstrations [2][3] - The standardization of mainstream agent communication protocols is facilitating multi-agent systems (MAS) to tackle complex tasks collaboratively, thus becoming a critical infrastructure in research and industry [3] Group 2: AI's Role in Research and Industry - AI is evolving from a supportive tool to an autonomous researcher, termed "AI Scientist," which will significantly accelerate the development of new materials and pharmaceuticals through the integration of scientific foundational models and automated laboratories [4] - The competition for consumer AI super applications is intensifying, with major players like OpenAI and Google leading the way in creating integrated intelligent assistants, while domestic companies like ByteDance and Alibaba are actively building their ecosystems [4][6] - The enterprise-level AI applications are entering a "valley of disillusionment" due to data and cost issues, but a turnaround is expected in the second half of 2026 as data governance and toolchain maturity lead to measurable value products in vertical industries [7] Group 3: Data and Performance Optimization - The rise of synthetic data is becoming crucial for model training as high-quality real data faces depletion, particularly in autonomous driving and robotics, where synthetic data generated by world models will be key assets [8] - The efficiency of inference remains a core bottleneck for large-scale AI applications, with ongoing algorithm innovations and hardware advancements driving down costs and improving energy efficiency, enabling high-performance models to be deployed at the edge [9] - The development of a compatible software stack for heterogeneous chips is essential to break the monopoly on computing power and supply risks, with platforms like Zhiyuan FlagOS aiming to create an open and inclusive AI computing foundation [10] Group 4: AI Security and Risk Management - AI security risks have evolved from "hallucinations" to more subtle "systemic deception," with ongoing research and industry efforts focusing on understanding model mechanisms and establishing comprehensive security frameworks [11]

趋势3：多智能体系统决定应用上限，Agent时代的"TCP/IP"初具雏形 复杂问题的解决依赖多智能体协同。随着MCP、A2A等通信协议趋于标准化，智能体间拥有了通用"语 言"。多智能体系统将突破单体智能天花板，在科研、工业等复杂工作流中成为关键基础设施。 趋势4：AI Scientist成为AI4S北极星，国产科学基础模型悄然孕育 AI在科研中的角色正从辅助工具升级为自主研究的"AI科学家"。科学基础模型与自动化实验室的结合， 将极大加速新材料与药物研发。报告强调，我国需整合力量，加快构建自主的科学基础模型体系。 趋势5：AI时代的新"BAT"趋于明确，垂直赛道仍有高盈利玩法 经济观察网2026年1月8日，北京智源人工智能研究院发布年度报告《2026十大AI技术趋势》。报告指 出，人工智能的演进核心正发生关键转移：从追求参数规模的语言学习，迈向对物理世界底层秩序的深 刻理解与建模，行业技术范式迎来重塑。 趋势1：世界模型成为AGI共识方向，Next-State Prediction或成新范式 行业共识正从语言模型转向能理解物理规律的多模态世界模型。从"预测下一个词"到"预测世界下一状 态"，NSP范式标志着 ...

Core Insights - The article discusses the convergence of life sciences and digital internet technologies through AI for Science, highlighting the transformative potential of large models in accelerating scientific discovery [3][6]. - It emphasizes the shift from traditional trial-and-error methods in drug development, which typically require 10 years and $2 billion, to automated processes enabled by AI, significantly reducing costs and time [7][8]. Group 1: Background and Framework - The 1950s saw two revolutions: Shannon and Turing's information theory laid the groundwork for the digital revolution, while Watson and Crick's discovery of the DNA double helix initiated the information age in biology [6]. - The article introduces a mapping framework for understanding AI in life sciences, with axes representing Generalist vs. Specialist and Tech vs. Bio, assessing the breadth and depth of startups in biopharmaceutical development [9][11]. Group 2: Biology Foundation Models - AlphaFold 3 represents a milestone in AI for science, solving the long-standing challenge of protein structure prediction, which previously took months or years [14]. - Isomorphic Labs, a spinoff from Google DeepMind, has secured significant partnerships with Eli Lilly and Novartis, validating its technology's commercial value [15]. - Other models like ESM3 and Evo2 are exploring different paths in biological foundation models, focusing on multi-modal inputs and genome language modeling [17][22]. Group 3: AI Scientist and Automation - The AI Scientist concept aims to automate research processes, addressing the inefficiencies of traditional biological research, which is often lengthy and costly [24]. - FutureHouse is developing a multi-agent system to enhance research efficiency, demonstrating the potential for AI to significantly increase productivity in scientific discovery [38]. Group 4: AI-native Therapeutics - AI-native therapeutics companies aim to integrate AI throughout the drug discovery and clinical development process, focusing on complex therapies like RNA and cell therapies [40]. - Companies like Xaira Therapeutics and Generate Biomedicines are building comprehensive platforms that leverage AI for end-to-end drug development, aiming to reduce time and costs associated with traditional methods [49][51]. Group 5: AI Empowered Solutions - Companies in this category focus on optimizing specific stages of drug development using AI, such as drug repurposing and clinical trial acceleration [68][75]. - Tahoe Therapeutics has released a large single-cell perturbation dataset, enhancing AI model training and drug discovery processes [64]. Group 6: Conclusion - The article concludes that the integration of foundation models and automated AI scientists is driving exponential advancements in scientific exploration, shifting value from traditional CROs to AI-native companies [78].