智源《2026十大 AI技术趋势》：“技术泡沫”是假命题，具身智能将迎行业“出清”

Core Insights - The focus of AI foundational model competition has shifted from "how large the parameters are" to "whether it can understand how the world operates," indicating a transition from merely predicting the next word to predicting the next state of the world [1] - AI is moving from "functional imitation" to "understanding the laws of the physical world," suggesting a clearer development path as it integrates into the real world [1] Group 1: 2026 AI Technology Trends - The ten major AI technology trends for 2026 include: 1. World models becoming a consensus direction for AGI, with Next State Prediction (NSP) potentially emerging as a new paradigm [2] 2. Embodied intelligence entering industry selection and implementation phases, moving beyond laboratory demonstrations [2] 3. Multi-agent systems determining application limits, with the initial formation of a "TCP/IP" for the Agent era [2] 4. AI's role in research evolving from a supportive tool to an autonomous "AI scientist," with domestic scientific foundational models quietly emerging [2] 5. A clearer new landscape for leading players in the AI era, with high-profit opportunities still available in vertical tracks [2] 6. Industry applications entering a "disillusionment valley," with a "V-shaped" recovery expected in the second half of 2026 [2] 7. The rising proportion of synthetic data, which is expected to break the "2026 depletion curse" [2] 8. Reasoning optimization has not yet peaked, and the "technology bubble" is a false proposition [2] 9. The open-source compiler ecosystem gathering collective intelligence, with heterogeneous full-stack foundations leading to inclusive computing power [2] 10. AI security evolving towards mechanisms that are explainable and self-evolving in response to deception [2] Group 2: Key Developments in AI - The report addresses the prevalent "bubble" debate in the industry, asserting that reasoning efficiency remains the core bottleneck and competitive focus for large-scale AI applications, with "technology bubble" being a false proposition [3] - Algorithmic innovation and hardware transformation are driving down reasoning costs and improving energy efficiency, making high-performance model deployment feasible at the resource-constrained edge [3] - Synthetic data is becoming the core fuel for model training, particularly in autonomous driving and robotics, supported by the "corrective expansion law" [3] Group 3: Transition to Physical World - The year 2026 is identified as a critical watershed for AI, marking the transition from the digital world to the physical world and from technical demonstrations to scalable value [4] - This transition is driven by three clear mainlines: 1. The "elevation" of cognitive paradigms, with AI beginning to learn physical laws, providing a new cognitive foundation for complex tasks like autonomous driving simulation and robot training [4] 2. The "embodiment" and "socialization" of intelligence, with humanoid robots entering real production scenarios, indicating that embodied intelligence is moving out of laboratories [4] 3. The "dual-track application" of value realization, with a super application portal forming on the consumer side and measurable commercial value products emerging in vertical fields on the enterprise side [4]