Against the backdrop of rapid iteration in the electronics and information industry, as well as continuous expansion in new energy and advanced manufacturing, connectors – acting as the “neural hubs” of electronic devices – are quickly evolving toward miniaturization, high-speed operation, high voltage, and greater precision. However, behind this thriving industry lies an increasingly evident crisis of generational gap in technical talent, which has become a core bottleneck constraining the sector’s high-quality development.
According to industry observations, the current talent structure in the connector sector exhibits a distinct gap: engineers born in the 1980s remain the core force behind mold design, product development, and process optimization, carrying the vast majority of the industry’s key technologies and practical expertise. The number of technical professionals born in the 1990s has significantly declined, making it difficult for them to smoothly take over the technological baton. Meanwhile, there is a massive shortage of new-generation technical talent from the 2000s onward. As a result, the industry as a whole faces a critical transition crisis – experienced professionals are aging, mid-career talents are insufficient, and fresh blood is lacking. Industry data shows that engineers skilled in IP67 protection design for automotive connectors and temperature control for high-voltage connectors are in severe national shortage; experts capable of designing high-speed differential signals above 100 Gbps are even scarcer – truly “hard to find.”
The shortage of technical talent directly leads to slow R&D iteration, high mold trial costs, and long product launch cycles for enterprises – particularly small and medium-sized connector manufacturers, which often miss market opportunities due to insufficient technical personnel. To overcome this challenge, companies cannot simply wait for talent to flow in naturally; they must take proactive steps. Based on the current industry landscape, adopting proven cross-sector models, integrating industry resources, and leveraging AI technologies to build a three-dimensional solution combining “talent development, resource sharing, and intelligent support” is key to resolving the talent crisis in the sector.
01
Why is the Talent Crisis Getting Worse?
Before delving into solutions, it is essential to first clarify the root causes of the talent crisis.
First, the generational gap in knowledge transfer has created a “broken chain.” Seasoned technical professionals with years of experience in the connector industry possess critical expertise and extensive know-how, serving as the core carriers of technological continuity within companies. However, reality shows that they are rapidly leaving – some poached by competitors with high salaries, others switching careers due to burnout and entering emerging industries. When experienced employees depart, they not only take their technical knowledge with them but also disrupt the team’s established stability and collaborative synergy. Even more concerning is that senior technical talents born in the 1980s are no longer young; within the next decade, this vital workforce will gradually enter retirement or semi-retirement.
Second, there is a disconnect between universities and industry, resulting in a gap between professional education and actual industry needs. Most domestic universities offering electronics and mechanical engineering programs focus their curricula primarily on fundamental theoretical instruction, with little coverage of specialized knowledge in the connector field. Practical training is also weak – laboratory equipment at universities is outdated and unable to simulate real-world conditions such as high-precision machining and complex performance testing involved in connector manufacturing. As a result, graduates often need to spend considerable time and effort relearning on the job, while companies are forced to incur additional costs for secondary training.
Third, companies are short-sighted and insufficiently invest in talent development. To cut costs, some enterprises hardly conduct internal training, leaving new employees to learn on their own after joining. Even when training is provided by certain companies, it often remains superficial, with outdated content disconnected from the latest industry technologies and processes. Training methods are also limited, primarily consisting of theoretical lectures, lacking practical exercises or case studies. Regarding career development, many companies fail to establish clear career paths for employees, who consequently see no upward mobility and thus lack motivation to stay long-term.
02
Geely Model
A Prototype of “Industry-Driven Education”
Faced with talent challenges, the connector industry might look to the automotive sector for inspiration. Geely Holding Group has delivered a highly instructive model of talent development through nearly three decades of practice.
Geely’s approach to education is essentially a product of Li Shufu’s philosophy of “using industry to run education and practical experience to cultivate talent.” To address the shortage of skilled personnel in key positions at the front lines of automobile manufacturing, Geely established an educational pathway – building schools wherever its industrial bases were located, creating a unique “factory on one side, school on the other” model of integrated industry-education collaboration. This model continues to play a distinctive role in vocational talent development today.
Geely’s core approach can be summarized as the “three-campus integration” – cross-sector, cross-regional, and cross-channel. The cross-sector campus transforms Geely’s satellite factories, AI labs, and other cutting-edge industrial facilities into classrooms; the cross-regional campus integrates global educational and industrial resources; and the cross-channel campus enables deep integration of online and offline learning. At the heart of this model is turning factories into real classrooms, allowing students to learn while gaining paid hands-on experience – rather than starting from scratch after graduation. Each vocational college is backed by a Geely production base, with curriculum design led by enterprise technical experts and case studies drawn directly from production sites, where lab equipment doubles as actual production machinery. Over decades, Geely has trained nearly 300,000 graduates, establishing a multi-tier talent development pathway ranging from secondary vocational education to postgraduate programs.
So, is this model replicable in the connector industry? The answer is yes.
For leading enterprises, jointly establishing a “Connector Industry College” with similar companies is entirely feasible. By leveraging industry associations and partnering with several vocational institutions, specialized programs in connector design, mold development, and precision manufacturing can be launched. The curriculum will be led by enterprise technical experts and incorporate real-world industry cases, such as signal integrity design for high-speed connectors and temperature rise control for high-voltage connectors.
Companies offer scholarships and internship stipends, allowing students to participate in real corporate projects while still in school and directly join the company upon graduation. If leading enterprises collaborate with local vocational colleges to establish “order-based training programs,” annually cultivating 20 to 30 mold engineers and product development engineers, the talent shortage will be fundamentally alleviated within three to five years.
For small and medium-sized enterprises, although they lack the capacity to establish independent educational institutions, they can replicate the “small but refined” aspects of Geely’s model:
First, build an internal training system by having experienced technical experts from the post-80s generation organize knowledge frameworks for core positions such as mold design and process parameter tuning, developing standardized courses. New employees then progress through these courses step by step, mastering each module in sequence.
Second, implement a standardized mentor-apprentice pairing mechanism, clearly define the mentoring period and evaluation criteria, and incorporate talent development into management and performance assessments.
Third, collaborate with vocational schools to conduct on-the-job internships, identifying promising new talent early through real-world projects.
03
Industry Collaboration
Activating Existing Talent with a “Living Chess Game”
Autonomous education addresses the issue of “incremental” talent development, but enterprises today are facing the urgent challenge of “insufficient existing talent.” While waiting for talent to grow, it is essential to rely on industry-wide collaboration and coordination to quickly activate current human resources.
The Shenzhen Connector Industry Association has already taken exploratory steps in this area, clearly identifying talent development as one of its core strategic priorities. At the proposal and with the support of Chen Yuxuan, chairman of Dianlian Technology, the association launched China’s first “Electrical Contact Fundamentals Elective Class” for undergraduate students at Shenzhen University of Technology, promoting in-depth collaboration among industry, academia, research, and application. Member companies have also actively responded by organizing public-interest technical training programs focused on common industry challenges, emphasizing the cultivation of highly skilled and specialized professionals, and taking concrete actions to strengthen talent pipeline development across the industrial chain.
On this basis, industry collaboration can be further advanced along the following pathways:
Establish an industry technical talent standard system. Led by industry associations and jointly involving leading enterprises and vocational colleges, define the skill requirements and knowledge frameworks for core positions such as mold engineers and R&D engineers. This addresses the long-standing issue of “confused training standards and poor alignment between supply and demand.”
Establish a public training and practical training platform. Leading enterprises in the industry can share their internal mold design processes, technical standards, and quality systems with small and medium-sized enterprises through an association platform. This allows SMEs to access training at low cost, thereby reducing the overall industry training expenses. The collaboration between Duanpin Precision and Dongguan Institute of Mechanical and Electrical Engineering is a pioneering example of this approach.
Promote flexible employment mechanisms. Senior engineers born in the 1980s can serve as technical consultants across companies, guiding mold design and resolving process challenges; key technical personnel born in the 1990s can be temporarily assigned to support urgent projects at sister companies. The association has established an industry talent pool, matching technical consultants, outsourced specialists, and full-time professionals according to demand, breaking down talent barriers between enterprises and maximizing the value of existing human resources.
Industry-level resource integration can not only quickly alleviate talent shortages among small and medium-sized enterprises, but also foster an industry ecosystem characterized by “shared talent and shared knowledge.” This transforms the experience and lessons gained by individuals across multiple companies from isolated knowledge silos into a collective asset for the entire industry.
04
AI Agent
The Ace in the Hole for Technological Replacement
If autonomous education and industry collaboration address long-term and mid-term challenges, then the technological enhancement provided by AI agents offers the most direct and efficient innovative solution to currently alleviate talent shortages and improve technical efficiency.
In the era of AI intelligence, specialized AI-powered design agents can take on tasks that were previously highly dependent on extensive professional experience. Traditionally, activities such as connector product modeling, mold structure design, optimization of gates and cooling systems, and simulation-based defect prediction relied heavily on engineers’ years of hands-on expertise, making it difficult for newcomers to get up to speed quickly. The shortage of experienced personnel directly constrains production capacity and R&D efficiency.
Dongguan Duanpin Precision is a benchmark in AI-powered intelligent agents for mold manufacturing. In collaboration with the Dongguan New Generation Artificial Intelligence Industry Technology Research Institute, the company leverages its extensive industrial field data and domain expertise to structure data and knowledge, building specialized large models and multiple business-oriented intelligent agents for industrial molds, which have now been fully integrated into mold production workflows. In the process programming stage, an intelligent programming agent based on a knowledge base automatically decomposes engineering drawings and assists in code generation, increasing programmer efficiency by 20%. In 2025, the company launched a new project titled “Duanpin High-Precision Mold AI Manufacturing Intelligent Agent Pilot Platform,” further advancing the integration of AI capabilities.
Another inspiring case is Liu Weixin, a model worker from Dongguan, who applied AI in the field of mold design. After spending a year on-site, working and collaborating closely with frontline engineers and master mold makers, he transformed the accumulated, unstructured expertise of experienced craftsmen into quantifiable and computable AI models. He developed an “intelligent mold design agent” based on large- and small-model technology, creating a closed-loop system that bridges traditional knowledge transfer with intelligent decision-making. “In the past, it took a master craftsman five to ten hours to design a simple mold; now our AI does it in just a few minutes.”
This system can generate mold design solutions with a single click, increasing design efficiency by 40% and reducing the production cycle from two months to just 36 days. “Many AI solutions require companies to purchase new equipment worth hundreds of thousands or even millions – how can small and medium-sized enterprises afford to make such transitions?” His strategy is “single-point breakthrough” – without replacing equipment or changing processes, simply installing an “industrial crayfish” on existing machinery to give old equipment a “new brain.”
At the international forefront, HARTING has partnered with Microsoft and Siemens on a joint project to apply AI-driven generative engineering design tools to customized industrial connector development. Engineers describe technical requirements in natural language, which the AI then converts into technical specifications and generates custom 3D models. AI-assisted design also helps conserve materials and reduce installation space, supporting green and sustainable manufacturing systems. Furthermore, AI agents can deeply integrate decades of enterprise design cases, process expertise, and error-correction data to build a company-specific knowledge base. New engineers no longer need prolonged trial-and-error learning; instead, they can leverage AI to reuse proven solutions and quickly resolve fundamental technical issues, significantly shortening their learning curve.
Practical implementation recommendations: large, medium, and small connector enterprises should choose different development paths based on their resource endowments.
Large enterprises: Customize industry-specific AI agents by integrating historical corporate data and design expertise to build private knowledge bases, enabling full-process AI empowerment – from product design to mold development and process optimization – driving tool upgrades and enhancing innovation efficiency.
Small and medium-sized enterprises: Start with lightweight AI tools instead of rolling out a full system at once. AI-assisted quoting can immediately improve efficiency, while intelligent programming support reduces reliance on skilled programmers. If you’re still hesitant, begin by testing an AI tool on a single aspect of mold design – such as gate position optimization – master one process before moving on to the next.
05
How can the Three Pathways Work Together Effectively?
To overcome the talent shortage, we cannot rely on a single solution but need to adopt a three-pronged approach and advance it in layers.
In the short term, relying on AI agents to fill labor gaps with technological tools is the fastest way to achieve results. However, this only addresses the issue of “lack of manpower” and cannot replace the intergenerational transfer of knowledge. The accumulated experience and process judgment skills in mold processing, stamping, injection molding, and other operations still require human expertise.
In the medium term, industry collaboration and shared resources can help activate existing talent. This is a path that can be quickly promoted within the industry by establishing talent development systems and sharing mechanisms through association platforms – low-cost and widely applicable. However, the essence of talent sharing is “borrowing intelligence,” not cultivating talent in-house, and thus cannot fundamentally resolve the long-term crisis of generational gaps.
Relying on independent operation to build a talent “self-sustaining ecosystem” is the most fundamental, challenging, yet thorough approach. The industry-academia integration system that Jili has developed over three decades with investments exceeding 10 billion yuan not only represents the long-term learning direction for the connector industry’s future, but also serves as the true “stabilizing force” ensuring the industry’s enduring growth.
In an ideal scenario, the three parties would have clearly defined roles: large enterprises would lead in establishing schools and building systems, taking the primary responsibility for training industry talent; industry associations would serve as platforms to promote cross-enterprise resource sharing and information exchange; and small and medium-sized enterprises would actively participate and leverage these resources to foster development, creating a virtuous cycle of “learning for application and application driving learning.”
Conclusion
Dual Evolution of Talent and Technology is Essential to Navigate through Cycles.
Talent is the core foundation for industrial upgrading, and intelligence is the key driver for industrial breakthroughs.
The talent gap in the connector industry is not merely a short-term labor shortage, but a structural contradiction rooted in long-term generational succession and technological evolution. From short-term AI supplementation to mid-term industry-wide sharing, and ultimately long-term independent education initiatives, the sector requires a comprehensive solution that integrates short- and long-term strategies with internal and external collaboration.
At the intersection of AI and industry, companies must not only embrace technological change but also proactively design pathways for human evolution. As Li Shufu stated: “With the arrival of the age of artificial intelligence, the education system developed over centuries is undergoing a transformation – one that will have profound impacts on corporate sustainability and the entire socio-economic landscape.”
It is hoped that this article will provide a clear framework for addressing the talent challenges in the connector industry, helping more companies find their own path to breakthrough.