Core Viewpoint - Musk predicts that in five years, there will be no more smartphones and apps, emphasizing a shift towards intention-driven human-computer interaction rather than manual input [2] Investment Perspective - The value of Musk's predictions lies not in the timeline but in highlighting the irreversible trend towards advanced human-computer interaction [2] - Investors are encouraged to focus on sectors such as neural interaction and computational scheduling to capitalize on this evolving trend [2]

Investment Rating - The report does not explicitly provide an investment rating for the industry discussed Core Insights - The 2025 World Artificial Intelligence Conference highlights a significant paradigm shift from a "model-driven" approach to an "intent-driven" approach in artificial intelligence, emphasizing the integration of human goals and values with AI processing [1][11] - Intent-driven intelligence aims to enhance decision-making reliability by incorporating causal reasoning and self-checking capabilities, moving beyond mere statistical outputs to achieve "purpose rationality" [2][12] - Current limitations of the model-driven paradigm, such as hallucination issues and diminishing marginal returns, necessitate breakthroughs at the paradigm level rather than just increasing computational power [3][13] Summary by Sections Section 1: Paradigm Shift - The transition from model-driven to intent-driven intelligence is characterized by the system's ability to autonomously identify and decompose goals without explicit instructions, integrating human values deeply into AI processing [1][11] - This shift requires AI systems to not only generate statistically valid outputs but also to possess capabilities for causal reasoning and self-correction to enhance decision-making reliability [2][12] Section 2: Challenges and Limitations - The report identifies key challenges in realizing the intent-driven paradigm, including the hallucination problem in large models, which threatens decision-making safety and raises ethical concerns [3][13] - The diminishing returns from merely increasing model parameters and data highlight the need for innovative approaches to overcome inherent limitations in current AI systems [3][13] Section 3: Technical Bottlenecks - Three major technical bottlenecks are identified: intent representation, causal reasoning mechanisms, and innovative learning architectures, which are essential for achieving the intent-driven paradigm [4][15] - Addressing these challenges is crucial for developing intelligent systems capable of general task modeling, maintaining decision-making robustness, and achieving deep collaboration with humans [4][15]