对话第一批AI原生劳动力：传统简历变废纸 什么才是AI时代的入职通行证

This article examines the diverging job search outcomes of the first generation of AI-native workers following the AI boom of 2026, and summarizes core requirements and practical guidance for job hunting in the AI era:

1. Cognitive shift: Job seekers must understand what AI capabilities enterprises actually demand. Treating AI as nothing more than an advanced search box or copy editor can no longer meet expectations. Employers prioritize the ability to solve real-world problems with AI, and AI-powered project portfolios are far more persuasive than traditional resumes and high GPAs.

2. Core competency development: Three core skills should be prioritized. First, aesthetic and judgment capacity to evaluate whether AI output meets requirements after generating results; second, domain-specific professional expertise, which is the core irreplaceable competitive advantage AI cannot substitute; third, the ability to deconstruct problems and implement solutions with AI.

3. Practical strategies: Non-technical graduates can pursue flexible alternative career paths, such as transitioning to AI output calibration and evaluation, building professional networks by organizing cross-industry events, and accumulating personal credibility by sharing AI-created content on social media.

This article outlines new shifts in the AI-era labor market and consumer landscape, with actionable insights for brands adjusting their hiring and marketing strategies:

1. Refine hiring criteria: Nearly all roles now involve AI. Year-over-year growth in job postings explicitly requiring AI skills hit 215.61%, and total AI-related job openings have surged 12-fold year-over-year. Brands should shift screening logic: stop overprioritizing degrees and GPAs, and instead prioritize candidates with AI-powered work samples demonstrating the ability to solve real brand marketing and operation problems. Evaluation should center on candidates' aesthetic judgment and domain expertise in branding.

2. Unlock new marketing opportunities: A large number of AI-native young creators now use AI to assist content creation and build personal content IPs on social platforms. The content produced by these independent creators is more authentic and engaging. Brands can explore partnerships with this group to open new marketing channels and reach broader younger consumer segments.

3. Align with emerging consumption trends: As a new generation of consumers, AI-native workers have deeply integrated AI into their daily habits. Brands need to adjust product development and marketing strategies to fit this group's AI usage patterns.

This article analyzes new shifts in the AI-era job market, and outlines opportunities and key considerations for sellers operating in AI-related sectors:

1. Market shifts: Demand for AI-related roles is growing explosively. New AI job postings rose 12-fold year-over-year in 2026, accounting for 26.23% of all new economy positions, and over 80% of campus recruitment openings at leading enterprises are AI-related. The AI job search and AI skill upskilling sector holds enormous growth potential.

2. Opportunity insights: A large share of AI-native graduates have unmet demand for job search skill improvement, advanced AI tool training, and resume optimization to align with employer requirements. Many non-technical graduates also want to transition into AI-related roles. Sellers can develop targeted products and services to address these segmented demands.

3. Risk warnings: Basic AI usage services are already being phased out of the market. Content that only teaches basic prompt engineering and tool operation no longer meets user needs. To gain market traction, offerings must focus on solving practical job search problems and building core competencies.

This article's analysis of AI-era competency requirements and industry shifts offers key insights for factories advancing digital transformation and adjusting hiring strategies:

1. Adjust hiring requirements: As AI is integrated into production, design, and operations across the factory, facilities do not need to prioritize pure AI technical talent. Instead, factories should prioritize candidates who understand factory-specific operations and can combine AI to solve practical production and design problems. Screening can focus directly on candidates' relevant AI work samples, rather than overvaluing degrees and traditional resumes.

2. Insights for digital transformation: Factories do not require all employees to master advanced AI techniques to implement AI applications. Basic AI operations can be quickly taught through standardized training. The core focus should be on cultivating employees' professional expertise and judgment capacity to evaluate whether AI output fits the factory's actual operational needs.

3. New business opportunities: AI has become a general-purpose infrastructure similar to electricity and water. Factories can use AI to optimize product design and production processes to cut costs and improve efficiency. They can also develop new AI-powered products aligned with market demand to unlock new growth opportunities.

This article identifies new trends and unmet customer needs in the AI-era job search service sector, and outlines clear development directions for relevant service providers:

1. Industry trends: The AI-era job market has undergone fundamental changes. Traditional resume coaching and basic job search services no longer meet the needs of the new generation of AI-native graduates, and the market urgently needs new services aligned with AI-era hiring requirements.

2. Core customer pain points: Most AI-native graduates only master basic AI usage, do not understand employers' actual talent requirements, and do not know how to build personal competitiveness with AI. A large number of non-technical graduates seeking to transition into AI-related roles lack clear transition roadmaps. None of these demands are currently well served.

3. Solution directions: Service providers can develop customized AI upskilling courses for graduates across different majors, with a focus on building the ability to solve practical problems with AI. They can also offer AI portfolio building services, and connect job seekers with employers. For non-technical graduates, providers can offer specialized cross-field job search coaching to help candidates reframe their experience to match AI industry role requirements.

This article's analysis of new shifts among job seekers and employers in the AI era offers actionable insights for recruiting platforms and AI-related platforms adjusting their operations:

1. Optimize product features: As the number of AI-native job seekers grows, traditional resume display formats no longer meet employer needs. Platforms can add a dedicated section for job seekers to showcase their AI-powered project work, enabling candidates to display results and helping employers screen candidates faster, improving supply-demand matching efficiency.

2. Adjust business development and recruitment strategies: Platforms can onboard targeted AI skill training and AI job search coaching service providers, and build a dedicated service section for non-technical graduates seeking cross-field transition into AI roles to meet segmented user demands. They can also partner with employers to host dedicated AI job fairs to attract more users.

3. Mitigate reputational risk: Platforms should guide users to correctly understand core competencies in the AI era, avoid overhyping AI's technical barriers, and communicate the correct message that professional expertise, judgment, and aesthetic ability are the real irreplaceable core competitiveness, to prevent misleading job seekers.

This paper documents the latest changes in China's job market following the widespread adoption of large AI models, providing first-hand case data and new research directions for scholars studying AI's impact on the labor market:

1. New industry trends: AI has penetrated across all industries, and the first cohort of AI-native workers has now emerged. The labor market's screening logic has fundamentally shifted: from prioritizing degrees, GPAs, and traditional resumes to valuing AI project portfolios and practical problem-solving ability. This shift will also ripple through higher education, driving reform of talent development systems.

2. Emerging problems: China's higher education talent development system has not kept pace with changes in the job market. Many institutions still teach traditional skills, and large numbers of AI-native graduates lack the AI application capabilities employers require. This has created structural supply-demand misalignment in the labor market, with associated risks of structural unemployment.

3. New job search models: Many non-traditional job search pathways have emerged, such as graduates transitioning into AI roles by sharing AI-generated content on social media, organizing cross-industry events, or conducting AI product evaluations. The development and impact of these new job search models warrant further in-depth research.