The Academy of Artificial Intelligence (AAI) released its "Top 10 AI Technology Trends for 2026" (PDF file).

The "Top Ten in 2026" report released by the Beijing Academy of Artificial IntelligenceAITechnology Trends, focusing onartificialintelligentThe report points out that the transformation is moving from the digital space to the physical world.AIDevelopment is shifting from a race to scale parameters to understanding and modeling the underlying order of the physical world. Key trends include the rise of world models and the new paradigm of Next-State Prediction (NSP), and embodied...intelligentCommercialization and multipleintelligentbodySystem co-evolution,AIIn-depth application in scientific research, andAIThe report emphasizes the interpretability of security mechanisms, among other things. It highlights that 2026 will be a crucial year.AIThis is a crucial juncture for technology, moving from perception to cognition and from the laboratory to large-scale application, providing the industry with a clear anchor for technological exploration and industrial layout in the coming year.

Access to the Academy of Artificial IntelligenceTop 10 in 2026AITechnology TrendsOriginal PDF report file. Scan the QR code to follow and reply: 20260109

along withartificialintelligentThe focus has gradually shifted from a race to scale parameters to a deeper understanding of the nature of the physical world.AITechnological development is entering a new stage. The "Top Ten Technologies for 2026" report released by the Beijing Academy of Artificial Intelligence (BAAI) shows this trend.AIThe "Technology Trends" report analyzes technological evolution to provide the industry with key directions and trend insights for future development, helping practitioners better grasp these trends.AIThis is a critical turning point from the digital world to the physical world, and from technology demonstrations to large-scale value realization.

Trend 1: World model becomes the consensus direction for AGI, Next-State Prediction may become a new paradigm.

AIMoving beyond simply "predicting the next word" to "predicting the next state of the world," unlocking cognitive and planning abilities. World models become universal.artificialintelligent(AGI) key directions, drivingMultimodalLarge ModelMoving from perception to a deep understanding of the laws governing the physical world provides a new cognitive foundation for complex tasks.

Trend Two: Embodied BodyintelligentIndustry consolidation is underway, and industrial applications are expanding into a wide range of industrial scenarios.

BodyintelligentFrom the laboratory to real-world production scenarios, humanoid robots are accelerating their commercialization. The industry is entering a "cleansing" phase, with intensified competition among companies. Those possessing closed-loop evolution capabilities will be the ones that truly succeed.intelligentIt stands out in industrial and service scenarios.

Trend 3: MoreintelligentbodyThe system determines the application limit.AgentThe "TCP/IP" of the era was in its nascent stage.

manyintelligentbodyThe system (MAS) becomesAIThe key to the application is achieved through standardized communication protocols (such as MCP and A2A).intelligentbodySynergy. From monomersintelligentTowards the Groupintelligent,manyintelligentbodyThe system will play a vital role in complex tasks such as scientific research and industry, breaking through the limitations of traditional single-unit systems.intelligentThe ceiling.

Trend 4:AI Scientist becomesAI4S North Star: Domestic Scientific Basic Model Quietly Developing

AIIn scientific research, its role has evolved from an auxiliary tool to a key element of independent research.AIA scientist is someone who can independently complete hypothesis formulation, experimental design, and data analysis. Scientific foundational models and...automaticBy combining chemical laboratories with scientific research, we can accelerate the development of new materials and drugs. my country needs to speed up the construction of an independent scientific basic model system to narrow the gap.

Trend 5:AIThe new BAT (Baidu, Alibaba, Tencent) of the era is becoming clear, and there are still highly profitable strategies in vertical sectors.

C-endAIThe "All-in-One" entry point for super apps has become a focal point of competition among tech giants, who are actively building integrated solutions.intelligentAssistant. Meanwhile, high-profit opportunities still exist in vertical sectors (such as health and education).AIA new BAT (Baidu, Alibaba, Tencent) landscape is taking shape.

Trend Six: Industrial applications are sliding into a "disillusionment trough," with a "V-shaped" reversal expected in the second half of 2026.

Enterprise levelAIAfter the initial proof-of-concept hype, applications entered a period of disillusionment due to issues such as data and cost. However, with the maturation of data governance and industry standard interfaces, a turning point is expected in the second half of 2026, when a batch of truly measurable MVP products will be deployed on a large scale in vertical industries.

Trend 7: The proportion of synthetic data is rising, potentially breaking the "2026 exhaustion curse".

High-quality real-world data is becoming increasingly scarce, making synthetic data the core fuel for model training. Supported by the "modified expansion theorem," synthetic data...automaticFields such as driving and robotics have shown great potential and are expected to overcome the problem of data depletion.

Trend 8: Inference optimization is far from reaching its peak; the "technology bubble" is a false proposition.

Reasoning efficiency remainsAIThe core bottleneck for large-scale applications is being addressed through algorithmic innovation (such as quantization and pruning) and hardware transformation (such as in-memory computing), leading to a continuous decrease in inference costs. Inference optimization is...AIThe key to universal accessibility lies in continuously improving energy efficiency, which drives the deployment of high-performance models at the edge.

Trend Nine:open sourceThe compiler ecosystem brings together collective wisdom, and the heterogeneous full-stack platform leads to the widespread adoption of computing power.

To break the computing power monopoly, building a software stack compatible with heterogeneous chips has become crucial.open sourceThe compiler ecosystem is thriving, and operator development languages are becoming more unified. Platforms like FlagOS are committed to creating a software-hardware decoupled, open, and inclusive ecosystem.AIComputing power base.

Trend 10: From Illusion to DeceptionAISecurity-towards-mechanism: Explanable and self-evolving attack and defense

AISecurity risks have evolved from "illusions" into more insidious "systemic deceptions," making the safety threshold a matter of life and death. Technically, this requires understanding the underlying mechanisms of models (such as loop tracing); industrially, it necessitates building a comprehensive security protection system, internalizing security as a core principle.AISystemic immune genes.