Based on a comprehensive analysis of various AI large models and predictive information provided by relevant literature, this paper presents a cautiously optimistic forecast for the development prospects of AI4S over the next five years. After analyzing the automatic generation of top−tier conference papers by The AI Scientist−v2 platform, it is pointed out that there are inherent difficulties in achieving full automation of the research loop, and incomplete verification and drift in research objectives are obstacles that constrain the development of AI4S. From the perspective of computational theory, the root cause of the verification bottleneck in AI4S lies in the semi−decidability of scientific problems, and the boundary of verifiability is the boundary of automation. According to verifiability, scientific and technological problems can be divided into two major categories: R1 problems and R2 problems. The risk of AI4S lies in treating problems that should be governed as R2 problems as R1 problems. This paper sharply points out that AI4S may pose more urgent security risk than AGI, and the risk of AI4S is not "AI becoming smart", but rather "humans losing the right to withdraw ".

AI for Science (AI4S)  /  incomplete verification  /  goal drift  /  semi−decidability  /  R1 problem  /  R2 problem  /  AI governance