财经问题研究

当前，人工智能正推动科学发现从传统的经验试错阶段迈向智能化、自动化的新阶段，引发了一场深刻的范式革命。本文提出，科学智能（AI for Science）不仅是技术工具的升级，更是一场重塑知识生产方式的智能化革命，标志着第五科研范式的全面兴起。科学智能通过自主假说生成、闭环实验验证与端到端建模，显著提升了传统科研在知识整合、数据处理、理论拓展和实验验证等方面的效率，实现了科研效率的指数级跃升。尤为关键的是，科学智能的“规模法则”与“飞轮效应”正推动科研从分散化的“小作坊”模式向平台化、协同化和工业化模式转变。这一变革为破解中国在“卡脖子”领域长期受制于人的问题提供了全新路径，也为中国发挥全产业链优势、实现科技—产业高效转化带来历史性机遇。本文强调，中国必须超越支持单个项目的传统思维，加快构建以数据为基础、算力为支撑、模型为核心、应用为牵引的国家战略科技力量体系。本文建议设立跨部门协同机制，推动形成开放共享的智能科研生态，将“人工智能+”行动深度融入基础研究与产业创新全链条，抢占全球科技竞争制高点，为发展新质生产力提供核心引擎。

This paper employs a techno-economic logic to demonstrate that the essence of AI for Science(AI4S) represents an intelligent revolution in scientific discovery, following the intelligent transformation of industrial manufacturing. This paper first outlines the comprehensive landscape of AI4S, presenting its evolutionary trajectory from Level 0(no AI involvement) to Level 5(pioneering explorer), revealing how AI transforms from a passive “tool” to an active “researcher”. It then analyzes why AI4S can enhance efficiency. Traditional research faces four major bottlenecks: high labor and time investment, data processing dilemmas, theoretical framework constraints, and trial-and-error costs, while AI achieves exponential improvements in research efficiency through automated retrieval, data insights, interdisciplinary integration, and virtual experimentation. This paper further explores why AI4S can revolutionize scientific paradigms: through end-to-end modeling and overcoming the “curse of dimensionality”, AI is challenging reductionist thinking, driving a shift in scientific cognition from “explanation-first” to “prediction-first”, ushering in a new era of autonomous knowledge discovery by machines.This paper finds that AI4S is propelling scientific research into an era of“ scaling laws”. Just as automated assembly lines disrupted workshop production, AI-driven research process automation brings exponential efficiency gains that cannot be matched through conventional “involution” approaches. Leading nations are constructing integrated AI4S platforms combining data, computing power, models, and applications, potentially creating insurmountable innovation barriers. For China, however, this represents both challenge and opportunity: leveraging complete industrial chains and scaled manufacturing advantages, deep integration of AI4S with the real economy could achieve breakthroughs in “chokepoint” areas such as materials, pharmaceuticals, and industrial software, transforming manufacturing advantages into new momentum for basic research.The contributions of this paper are as follows. First, it unifies AI4S's technical principles, efficiency mechanisms, and paradigm revolution through a techno-economic framework, bridging the gap between technical literature and policy discussions. Second, it demonstrates AI4S's essential characteristics as an intelligent revolution and its “scaling law” effects. Third, it proposes China's unique pathway for developing AI4S through manufacturing advantages. Policy implications include establishing ministerial-level coordination mechanisms to orchestrate AI4S development to avoid “workshop-style” low-level duplication; reassessing research resource allocation using AI4S efficiency standards; promoting coordinated development of the four pillars—data, computing power, models, and applications—to ensure China is not “locked out” but rather seizes the initiative in this intelligent revolution of scientific discovery.