The onset of ChatGPT, Sora, Claude-3, and the like, has brought about the era of AIGC for text, images, and videos. Literature review, scientometric analysis, and S&T trends analysis have also being rapidly taken over by AI tools, thus making traditional knowledge services (KS) falling into a “low-quality productivity trap”. It is difficult to develop new quality productive forces with competitive vitality and resilience by only using AI to optimize the execution efficiency of traditional KS business logic. AI, as illustrated by Large Language Models (LLM), has broken the reductionist “research model” that disassembles complex phenomena and systems into individual parts to study and solve, and the turing computing model that pursues deterministic computing, therefore able to handle the dimensional disaster from the combinatorial explosion of complex multi-interactive systems. This helps us to truly take “complex problems, dynamic decision conditions, and selective operational solutions” as the goal of KS, and provides users with decision intelligence. This may be the starting point in the search for new quality productive forces in KS. But it is imperative to ask “what is the real problem” from the point of First Principle. Starting from the fundamental needs of decision users of KS, we need to think clearly about what KS should do, can do, and must do. Admittedly, when problems of decision-makers ask for various data or information analysis, what they really need is to answer is not just “what is” but “why is so and what can/should I do” in their S&T planning, organizing, resourcing, evaluating, etc., under their specific conditions. If so, KS should now be positioned as a “user decision-making productivity service”, focusing on Policy for S&T (P4ST), hence transforming KS from the literature-oriented or data-oriented or indicator-oriented to user-problem/solution-oriented models, and from data or computational intelligence to cognitive and decision intelligence. Based on several examples, this paper proposes a generalized decision-making genomic model for AI-empowered Policy for Science & Technology (AI4P4ST). The model consists of an Agent axis (multi-levels from individuals to nations), an Action axis (planning, organizing, budgeting, evaluation, etc.), and an Outcome axis (plans, institutions, teams, projects, papers, patents, products, etc.). Use of this model supports intelligent decision-making analysis under the dynamics of complex systems. With multiple combinations of variables that interact in known or unknown ways, we can perform multi-modal cross-scale modeling and analysis of multi-dimensional multi-variates, continuously adjusting to approximate possible solutions with quantifiable uncertainties, so that decision-makers can select for a decision. AI4P4ST analysis can progressively implement the P4ST analysis pipelines that supports the dynamics of complex systems. The LLM Prompt Engineering and its many augmented models can be used to build an AI4P4ST Chain of Analyses. In addition, technologies such as AI Agents, Multi-Agents Models, and Mixture of Experts (MoE) models, as well as mechanisms such as LangChain or GPTSwarm, can be employed to support AI-enabled application processes that combine multiple LLMs and specialized tools, thus enabling intelligent processes such as planning, prediction, experimentation, verification, and analysis for AI4P4ST. Of course, AI4P4ST still faces challenges from complex data environments and complex social dynamics, including multi-modal heterogeneous data environments, boundary uncertainty, strong game adversariality, difficulties in handling critical states, and weak counterfactual reasoning. This may require a combination of knowledge-based intelligence modeling, simulation and prediction based on the complex system dynamics, and data-based LLM modeling, and the use of LLM models to plan, coordinate, and support these modeling and analysis.