The 15th Five-Year Plan outline explicitly calls for accelerating advancements in digital and intelligent technologies while deepening the expansion of the "AI+" initiative. As artificial intelligence becomes deeply integrated with the real economy, fostering the intelligent economy has emerged as a pivotal strategy for cultivating new economic drivers and enhancing the overall quality of economic development. As a primary resource, the supply quality and allocation efficiency of talent directly determine the growth potential and momentum of the intelligent economy. Focusing on the new imperatives for expanding talent capabilities and reshaping educational paradigms, accelerating the construction of a support system tailored to these needs is essential to creating an environment that enables growth, mobility, and value realization for professionals, thereby providing robust talent empowerment for the intelligent economy.
Human-Machine Collaboration Introduces New Skill Requirements
Throughout human history, every technological revolution has given rise to new modes of production organization and new configurations of production factors. Today, artificial intelligence is spearheading a new round of technological and industrial transformation, driving innovative allocations of factors such as technology and labor and reshaping the fundamental logic of the division of labor. To a certain degree, AI is no longer merely a tool; it can be regarded as a new type of knowledge-producing entity, and the relationship between humans and machines is progressively evolving from "humans using machines" to "human-machine collaboration." Correspondingly, the core competitiveness of talent no longer hinges solely on the amount of pre-existing knowledge one possesses, but rather on the ability to effectively harness AI tools, make sound judgments in complex contexts, and leverage one's distinctive strengths within the division of labor between humans and machines.
On one hand, we must assess human-machine collaboration ability. From the standpoint of the evolving division of labor, human-machine collaboration is becoming the prevailing paradigm of labor distribution in the intelligent economy era. AI liberates workers from repetitive tasks, enabling them to shift toward higher-order activities that demand creativity, critical judgment, and empathetic interaction. This requires workers to develop an understanding of the underlying principles of AI technologies and to acquire the skills to utilize AI tools effectively, integrating technological outputs with their own professional expertise to achieve synergistic effects that surpass what either humans or intelligent machines could accomplish independently.
On the other hand, we must evaluate cross-disciplinary integration capability. The realization of value in the intelligent economy often depends not only on AI technology itself, but more critically on the deep convergence of AI with industry-specific knowledge and operational processes. This necessitates that talent forge connections between technology and business, algorithms and scenarios, and data and domain expertise, thereby achieving the integration and innovation of multi-disciplinary knowledge. This cross-disciplinary integration fundamentally reconfigures how knowledge is produced and applied, requiring individuals to establish linkages across diverse knowledge domains and execute knowledge recombination effectively.
Adapting to Change to Optimize Human Capital Supply
In the traditional industrial economy era, workers' productive capacity was predominantly expressed through the mastery of established knowledge and proficiency in standardized procedures, with talent cultivation centered on knowledge transmission and technical skill training. As we enter the intelligent economy era, the "half-life" of knowledge has shortened dramatically, and the pace of technological iteration has accelerated. Consequently, talent development approaches must proactively adapt to these shifts to achieve better alignment between the supply and demand of human capital.
First, the educational philosophy must transition from knowledge dissemination to cognitive construction. The educational philosophy determines the fundamental pathways and methodologies of talent cultivation. Traditional education is characterized by discipline-based knowledge transfer and standardized evaluation, underpinned by the implicit assumption that knowledge is relatively stable and transmissible. In the intelligent economy era, the barriers to knowledge acquisition have been significantly lowered, as AI can instantly generate, synthesize, and present knowledge content. This necessitates a transformation in education from one-way knowledge indoctrination to interactive cognitive construction and the cultivation of interdisciplinary talent.
Second, talent development mechanisms must evolve from superficial collaboration to deep-level linkage. The cultivation mechanism determines the interactive relationships and resource allocation patterns among various stakeholders in the talent development process. Traditional industry-education integration models often remain at a loose collaborative level without sufficient depth, primarily because education and industry operate under different institutional logics. Education emphasizes knowledge transmission and academic evaluation, while industry is oriented toward market value and efficiency enhancement. The divergence in objectives can result in structural mismatches between talent supply and industrial demand. The key to advancing from shallow collaboration to deep linkage lies in fostering a seamless connection between talent cultivation, technological advancement, and industrial development, establishing stable communication channels and trust mechanisms, and enhancing the relevance, innovativeness, and effectiveness of talent training through robust industry-education integration.
Third, ethical awareness must shift from passive indoctrination to active exploration. As AI technologies continue to iterate and unleash immense productive forces, they simultaneously introduce risks such as algorithmic bias and data privacy concerns. There is an urgent need to strengthen scientific and technological ethics and security governance, adhere to the value orientation of technology for social good, and cultivate professionals who are proficient in both technology and ethics. Notably, the complexity of contemporary ethical challenges far exceeds that of traditional ethical issues. Talent development cannot rely on rote memorization of rules; instead, values must be constructed through experiential learning, reflection, and dialogue within concrete practical contexts. This implies that beyond imparting foundational ethical knowledge, we must guide individuals in confronting ethical conflicts and deliberating on value trade-offs within authentic technological practice, gradually internalizing ethical norms as their own behavioral standards, and ensuring that the development of the intelligent economy better balances efficiency with equity, and innovation with security.
Focusing on Full-Cycle Development to Solidify the Talent Foundation
Looking ahead, building a high-quality talent pool that aligns with the intelligent economy requires grasping the evolving trends in digital talent demand, transforming cultivation models, and constructing a comprehensive support system spanning the entire life cycle, thereby fostering a thriving ecosystem conducive to talent development.
Improve a pluralistic and integrated talent growth mechanism. The core characteristic of the intelligent economy lies in the deep fusion of technology, industry, and application scenarios. We should move beyond rigid development pathways and establish inclusive and diverse channels for talent advancement, enabling individuals with varying expertise and knowledge backgrounds to identify suitable spaces for their own growth. Build open and collaborative innovation platforms that facilitate cross-field and cross-industry resource sharing and co-development of talent, allowing outstanding professionals to flow smoothly among diverse innovation entities, continuously learning in practice and growing through collaboration.
Refine a flexible and adaptive talent evaluation system. To better respond to the rapid pace of information technology evolution, we should adhere to differentiated evaluation approaches, setting distinct criteria based on the characteristics of various talent types—including basic researchers, applied developers, and interdisciplinary professionals—while incorporating algorithm models, open-source contributions, patent standards, and industrial implementation outcomes as key assessment factors. Support participation from multiple stakeholders such as industry, the investment community, and end users, integrating market recognition, user feedback, technology commercialization effectiveness, and industrial contributions into the evaluation framework. Maintain dynamic adjustment mechanisms, promptly updating evaluation standards in response to technological and industrial demand shifts, and establishing rapidly responsive adjustment processes.
Foster an innovation-encouraging talent cultivation environment. The iterative training of large models, the optimization and upgrading of algorithms, and the validation and deployment of new application scenarios all depend on sustained exploration supported by tolerance for trial and error. It is essential to guide society in rationally perceiving the setbacks and failures that talent may encounter during innovative pursuits, forming an innovative environment that rewards exploration, tolerates failure, and respects long-term thinking—thereby invigorating the creativity and initiative of talent. On a deeper level, human creativity, empathy, value judgment, and the capacity to holistically grasp complex situations remain unique strengths that AI finds difficult to replicate. We should further emphasize human agency in value orientation, empowering talent to maintain confidence and initiative throughout the progression of the intelligent economy.
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