Quick Navigation / Table of Contents

• Abstract and Core Findings
• Part 1: Macro Trends and Panoramic Overview
  • 1.1 The Paradigm Shift from "Tool Assistance" to "Agentive Collaboration"
  • 1.2 Strategic Layout of Major Global Economies in AI Competition and Labor Policies
  • 1.3 Analysis of Key Driving Forces: Technological Breakthroughs, Cost Reduction, and Accelerated Business Penetration
  • 1.4 Theoretical Framework and Impact Classification of AI on Jobs
  • 1.5 Macro Data and Forecasts of AI's Impact on Jobs in 2025
• Part 2: Meso-level Analysis—Industry, Region, and Position
  • 2.1 In-depth Impact on Industries: Cases and Data
  • 2.2 Analysis of Regional Differences: Responses and Challenges of Countries at Different Development Levels
  • 2.3 Dynamics at the Enterprise Level: Landmark Corporate Adjustments and Events in 2025
  • 2.4 Job Risk Quadrant: TOP Lists and In-depth Reasons
• Part 3: Micro-level—Skills, Compensation, and Individual Responses
  • 3.1 The Transformation of Education and Vocational Training Systems
  • 3.2 Changes in Salary and Labor Market Structure
  • 3.3 Analysis of the Impact of Different Technological Paths
• Part 4: Future Outlook, Social Impact, and Strategic Recommendations
  • 4.1 Evaluation of AI's Impact on Global Work Efficiency and Economic Benefits
  • 4.2 Deep Social Impacts of the Transformation of Work Forms
  • 4.3 Multi-level Recommendations for Individuals, Enterprises, and Governments
  • 4.4 Conclusion: Moving Towards a Future of Human-Computer Symbiosis
• Appendix: Core Data and Case Reference Tables

Core Viewpoint: In 2025, the impact of AI on human jobs has entered a new systematic and normalized stage. This is not a simple replacement of jobs, but a profound restructuring of the nature of work. The core of the transformation is the restructuring of the working relationship: from humans using tools to humans and intelligent agents coexisting in synergy.

Key Data:

• The International Monetary Fund (IMF) predicts that nearly 40% of global jobs will be affected by AI, with about 60% in developed countries, 40% in emerging markets, and 26% in low-income countries.
• The World Economic Forum (WEF) predicts that by 2030, 22% of jobs will change, with 170 million new jobs created and 92 million jobs replaced, a net increase of 78 million.
• The International Monetary Fund estimates that AI may contribute about 0.1 to 0.8 percentage points to the global economic growth rate each year, and may increase the global GDP level by 1.3% to nearly 4% in the next ten years.
• PwC's 2025 report: The average salary premium for jobs with AI skills is as high as 56%, more than double the previous year (25%).
• The United Nations Conference on Trade and Development (UNCTAD) warns that without urgent action, the benefits of AI will be captured by a few privileged groups, exacerbating the global digital divide.

Core Trends:

1. Paradigm Shift: From "tool assistance" to "agentive collaboration," AI is evolving from a passive tool to an autonomous "digital partner."
2. Global Divergence: AI is reshaping the geographical landscape of the global labor market. Developed countries hope to consolidate their leading positions, newly industrialized countries are striving to upgrade in the cracks, and developing countries are facing the great risk of being marginalized.
3. Skills Restructuring: The demand for AI literacy, prompt engineering, and systems thinking has become widespread, while the value of "human skills" such as creative thinking, critical judgment, emotional intelligence, and leadership is further highlighted.
4. Work Deconstruction: AI breaks down work into automatable subtasks and complex links that require human participation, promoting the migration of jobs to "human-computer collaboration nodes."

1. Individual Work Level: The Leap from "Q&A Machine" to "Executor"

Past AIs (such as traditional chatbots) mainly completed "word-guessing" information responses. Agents, on the other hand, are given "hands" and "eyes" to understand user instructions, autonomously plan steps, call various tools (such as ticket booking software, data analysis APIs), and complete a complete task loop from beginning to end. For example, it can not only recommend travel plans, but also directly complete ticket booking, hotel ordering, and itinerary generation. This means that the logic of a large number of white-collar jobs centered on "process" and "coordination" (such as administration, junior analysis, and customer service scheduling) is being rewritten.

2. Organizational Level: The New Normal of "Human-Machine Hybrid Teams"

The workflow is no longer designed around the linear rhythm of human employees, but is reconstructed for "human-led + agent execution". Human roles are upgraded to target setting, process supervision, and result adjudication, while agents are responsible for large-scale, precise execution of specific tasks. Domestic and foreign technology giants have already deployed agent platforms (such as Baidu's "Xinxiang" and ByteDance's "Kouzi"), which have sharply reduced the marginal cost of creating "digital employees" that serve specific business scenarios. Within enterprises, a model in which a human manager leads multiple professional agents (financial analysis agents, market report agents, code generation agents) to work together is becoming a reality.

3. Industrial Level: Deep Transformation of Value Creation Mode

When agents penetrate into all aspects of R&D, production, and service, they can not only improve efficiency, but also create, retain, and reuse knowledge at a speed and scale that is difficult for humans to achieve, forming a "knowledge compound interest". For example, in the manufacturing industry, a solution based on "AI Agent + industrial vertical model" can transform a heavy-duty engine manufacturing plant into a smart vehicle factory, increasing the efficiency of vehicle offline to the minute level. This change has sharply reduced the cost of "specialization", and small organizations can compete with giants in specific fields with highly specialized agents, which may subvert the industrial division of labor pattern for a hundred years.

1. Technological Breakthroughs: Expanding the Frontier from "Digital Intelligence" to "Physical Intelligence"

Current technological breakthroughs are running in parallel along two main lines, expanding the influence of AI from the virtual world to the physical world:

• Maturity of Agent Technology: As mentioned in Section 1.1, this is the most direct driving force for the current work paradigm shift. Agents solve the problem of large models having "brains but no hands", enabling them to truly "work". The development of multi-agent collaboration technology allows different agents to work together like a project team to handle extremely complex tasks (such as perception, planning, and control collaboration in autonomous driving).
• Breakthroughs in Embodied Intelligence and World Models: This is the more far-reaching "next wave". Represented by the WoW (World-Omniscient World Model) model open-sourced by the Beijing Humanoid Robot Innovation Center, the new generation of technology is committed to allowing AI to learn physical laws through interaction, achieving a leap from "generating content" to "executing actions". If the GPT series understands language and the Sora series sees the world, then models like WoW aim to let robots "understand and change the physical world with their hands." This indicates that AI will have a more direct intervention in jobs that require physical operations such as manufacturing, logistics, and medical surgery.

2. Cost Reduction and Accelerated Business Penetration: From "Experimental Product" to "Necessity"

Technological breakthroughs are rapidly translating into business momentum. On the one hand, through open source ecosystems and technological optimization (such as the DeepSeek large model, which has reduced R&D costs by 60%), the threshold for AI applications has been greatly lowered, allowing small and medium-sized enterprises to enter the game. On the other hand, the initial market education has been completed, and companies have found that AI is no longer a concept of "icing on the cake", but a "fire in the snow" that concerns survival efficiency. Deloitte predicts that by 2025, 25% of enterprises will deploy generative AI-driven agents. The demand has shifted from "what functions are available" to "what specific business problems can be solved", driving the deep penetration of AI in industries with a good digital foundation such as finance, manufacturing, and medical care. A landmark trend is that companies are redesigning their data systems and business processes for agents, not just for humans.

The New Form of "Human-Machine Collaboration": AI as Executor, Consultant, and Colleague

On the basis of the above classification, "human-machine collaboration" is no longer a vision, but a specific working mode. AI plays three core roles in collaboration:

• As "Executor": This is the most common form. Humans act as "commanders", responsible for defining goals, setting constraints, and acceptance criteria; AI acts as "soldiers", responsible for efficiently and accurately executing specific tasks.
• As "Consultant": AI plays the role of "expert think tank" or "sounding board". Humans propose complex problems or preliminary ideas, and AI provides multi-angle analysis, feasibility assessment, or alternative solutions based on a global knowledge base to assist human decision-making.
• As "Colleague": This is a more advanced form of collaboration. Humans and AI agents form project teams, each undertaking a part of the subtasks with autonomy, and interacting and correcting each other in real time during the process.

Comparative Analysis:

• Reasons for data differences: The IMF's "40%" refers to the proportion of affected jobs (including augmentation and replacement), which is a broader scope. The WEF's "22%" focuses more on the net change rate of the job structure and gives specific numbers of creation and replacement.
• Consistent trend: All institutions believe that AI will reshape rather than eliminate work, and its depth of impact is closely related to the "readiness" of countries, industries, and individuals.

Job Change Trends and Timeline:

Short-term (2025-2027): Job Polarization and Skills Shock

This stage is characterized by the simultaneous occurrence of replacement and creation, but there is a mismatch:

• Job polarization begins: Programmatic white-collar tasks (such as basic data entry, content editing, standard customer service) and some blue-collar repetitive labor will be squeezed first. At the same time, the demand in fields such as AI research and development, deployment, and ethical governance will grow rapidly.
• Enterprises are actively transforming: Leading companies such as Goldman Sachs are deploying AI tools on a large scale and adjusting their organizational structure to improve efficiency. This may lead to the streamlining of some jobs and the slowdown of recruitment in related fields.
• The skills mismatch is exacerbated: The World Economic Forum points out that 63% of employers believe that the skills gap is the main obstacle. The mismatch between the skills of the existing labor force and the new demands of the AI era will be the biggest social challenge in the short term.

Mid- to Long-term (2028-2030 and beyond): New Balance and Net Growth

• Net job growth: According to the World Economic Forum's model, by 2030, job creation will exceed replacement, resulting in a net increase of 78 million jobs. These new jobs will not only come from the technology industry, but also from fields that have expanded due to the popularization of AI (such as personalized education, health management) and brand new professions created by AI.
• "Human-machine collaboration" becomes the norm: As Goldman Sachs' CIO said, the workplace will shift to a "hybrid workforce". Human core responsibilities will shift to strategic planning, creative generation, ethical supervision, and complex interpersonal interactions, while AI will undertake execution, analysis, and large-scale tasks.
• Fundamental shift in skills demand: The demand for AI literacy, prompt engineering, and systems thinking will become widespread. At the same time, the value of "human skills" such as creative thinking, critical judgment, emotional intelligence, and leadership will be further highlighted.

Analysis of the Contribution to Global GDP Growth

The promotion of the economy by AI is significant, but its dividend distribution may be extremely unbalanced:

• Overall growth potential: The IMF estimates that AI may contribute about 0.1 to 0.8 percentage points to the global economic growth rate each year. Given that the current global growth rate is around 3%, this contribution is "quite considerable". Its long-term model shows that AI may increase the global GDP level by 1.3% to nearly 4% in the next ten years.
• Key driving paths:
  1. Direct productivity improvement: AI automates tasks and enhances decision-making, directly improving the output efficiency of all walks of life. For example, Goldman Sachs expects AI to increase programmer productivity by 3-4 times.
  2. Stimulate innovation and new industries: AI reduces the cost of R&D and creative trial and error, gives birth to new products, services, and business models, and opens up brand new markets.
• The risk of a "gap" in growth: The IMF has repeatedly warned that AI may exacerbate inequality between and within countries.
  • Gaps between countries: Developed countries with high readiness (such as the United States) may get the greatest benefits (the IMF model shows that their output may increase by as much as 5.6%), while countries lacking infrastructure, capital, and digital skills may fall further behind.
  • Gaps between groups: The income of high-skilled workers who can use AI to enhance themselves may grow rapidly, while workers engaged in automatable tasks will face pressure, which may exacerbate the gap between rich and poor.

Conclusion: AI is bringing about a profound revolution involving employment structure, skill value, and economic geography. Whether the technological potential can be transformed into broad economic and social well-being depends on the response of various countries in educational transformation, social security, and inclusive policies.

2.1.1 Knowledge-intensive Industries: From "Efficiency Tool" to "Productivity Core"

AI is becoming the new "productivity core" for industries such as software, finance, law, and media.

Software Engineering:

The penetration rate of AI programming assistants (such as GitHub Copilot) has exceeded 50%. Its impact goes far beyond code completion and is reshaping the development process: developers are shifting from "writers" to "system architects" and "code reviewers", responsible for proposing high-level designs, reviewing and integrating AI-generated modules. Research by Microsoft and MIT shows that developers using Copilot have a 55% increase in task completion speed, and confirms its "capability leveling effect" - less experienced developers benefit more.

Finance and Law:

In these two fields that are highly dependent on text and rule analysis, AI is realizing a "review revolution". AI tools deployed by institutions such as Deloitte can shorten contract review time from hours to minutes, with an efficiency increase of 90% and a significant reduction in the risk of human error. In the financial field, Morgan Stanley's AI assistant can analyze massive research reports in real time, providing investment advisors with precise insights and upgrading their services from information retrieval to in-depth consulting. Goldman Sachs is advancing its "Goldman Sachs 3.0" transformation plan, with the goal of achieving end-to-end automation of the core processes of its investment banking business.

Media and Content Creation:

Generative AI has entered the core of the workflow. BloombergGPT, developed by Bloomberg, is specially used for financial news generation and data analysis; the Associated Press has been using AI to write financial reports since 2014. Today, tools such as Midjourney and Sora are driving the marginal cost of content production to near zero, forcing practitioners to shift their core value to early creative planning, in-depth investigation, and exclusive viewpoints.

2.1.2 Manufacturing and Logistics Industries: From "Rigid Automation" to "Perception and Decision" Intelligence

The evolutionary path of this industry is from replacing physical labor with robotic arms to upgrading to AI systems replacing some mental labor to achieve global optimization.

Intelligent Manufacturing:

AI visual inspection systems can now identify microscopic defects that are difficult for the human eye to detect, increasing the accuracy of quality inspection to over 99.9%. More importantly, predictive maintenance systems based on machine learning can predict equipment failures days or even weeks in advance, reducing unplanned downtime by 30-50%. China's "lighthouse factories" demonstrate how AI can optimize the entire production system, achieving energy consumption reduction and optimal production scheduling.

Smart Logistics:

In the warehousing stage, AI dispatching systems can optimize the picking paths of robots in real time, increasing warehouse throughput by more than 300%. In the transportation stage, intelligent driving of trunk logistics and "warehouse-to-warehouse" unmanned delivery are accelerating their implementation. For example, JD Logistics' "intelligent brain" can now predict future orders in the tens of billions and schedule resources in advance.

2.1.3 Customer Service and Sales: The Transformation from "Cost Center" to "Growth Engine"

AI is transforming traditional passive responsive services into proactive, personalized, and full-cycle customer relationship management.

Intelligent Customer Service:

Early rule-based robots (answering "yes/no") have evolved into large model-driven assistants for emotional perception and complex problem solving. Leading enterprise AI customer service can understand ambiguous intentions, handle multi-turn dialogues, and have a first contact resolution (FCR) rate of over 70% and a customer satisfaction (CSAT) rate of up to 85%, which is comparable to or even better than human levels.

Intelligent Sales:

AI sales assistants can analyze customer profiles, communication records, and market dynamics in real time to provide salespeople with the best communication scripts, product recommendations, and pricing strategies. For example, Salesforce's Einstein AI can predict a customer's purchase propensity and churn risk. Data shows that sales teams using AI assistance have a 10-fold increase in lead response speed and an average increase of 30-40% in transaction rates.

2.1.4 Penetration of Traditional "Safe Zone" Industries: AI as a "Capability Multiplier"

In fields such as education, medical care, and scientific research that rely on human professional judgment, AI is not replacing experts, but becoming their "super assistant", amplifying their professional value.

2.1.5 The Birth of New Professions and Industries: Born for AI, Growing with AI

A brand-new professional ecosystem serving the AI system itself is rapidly taking shape.

Conclusion: AI is carrying out a silent but thorough "work deconstruction" - breaking down existing jobs into "automatable task combinations" and "human cores that must be retained". Future career competitiveness depends on whether individuals can quickly identify and integrate into new value chains and find irreplaceable anchors in the new ecosystem of human-computer collaboration.

2.2.1 Developed Countries: Advantages of High-skilled Labor and Risks of Structural Unemployment Coexist

Developed countries, represented by the United States, the European Union, and Japan, are most likely to be the first to capture the productivity dividends of AI due to their first-mover advantages in technology, capital, and high-end talent, but the risk of social division within them is also the most severe.

Advantage: Occupying the Top of the Value Chain

Developed countries have the world's top AI research institutions, leading companies, and venture capital. For example, the United States has an absolute dominant position in basic models (such as OpenAI's GPT series), chips (Nvidia), and ecosystems. This enables it to define technical standards and obtain the highest profit share. The proportion of high-skilled white-collar workers in its labor force is higher, and it is more likely to use AI as a "capability multiplier", thereby improving the overall productivity of individuals and the country. The International Monetary Fund (IMF) study predicts that the productivity improvement of AI in developed countries may be as high as twice that of emerging markets.

Core Challenge: Exacerbating Middle-Class Hollowing Out and Inequality

AI automation first impacts the large number of mid-end white-collar jobs in developed countries that perform programmatic tasks (such as administration, accounting, and legal assistants). A Goldman Sachs study points out that about two-thirds of current jobs in the United States are potentially affected by AI automation to some extent. This may cause "job polarization": high-paying creative management jobs and low-paying manual service jobs increase, while traditional middle-class jobs shrink, thereby exacerbating income inequality and social instability. According to the analysis of the Organisation for Economic Co-operation and Development (OECD), even within developed countries, regions where industries that rely on routine cognitive tasks are concentrated will face more severe employment shocks.

2.2.2 Newly Industrialized Countries: The Impact of Manufacturing Automation on Traditional Cost Advantages

Newly industrialized economies, represented by China, Southeast Asia, and some Eastern European countries, are at a critical crossroads of development. Their long-term reliance on "demographic dividends" and "cost advantages" is being directly threatened by AI-driven automation.

Severe Impact: The Foundation of the Traditional Growth Model is Shaken

The economic growth of these countries relies heavily on manufacturing exports, and the global supply chain is introducing cheaper "machine labor". For example, in fields such as electronics assembly and textiles and apparel, robots guided by AI vision and flexible automated production lines are making it economically feasible to "reshore" to developed countries or to achieve "unmanned factories" locally. Data from the International Federation of Robotics (IFR) shows that China has become the world's largest industrial robot market, which is both a symbol of industrial upgrading and a sign of accelerating replacement of traditional intensive labor. The World Bank warns that automation may cause emerging economies to face the risk of "premature deindustrialization", that is, losing a large number of manufacturing jobs before they can fully transition to high-value-added industries.

Core Challenges and Ways Out: The Difficult Race of Industrial Climbing

The core challenge for these countries is whether they can quickly cultivate enough high-skilled labor to design, maintain, and optimize these AI systems and engage in higher-value-added services while low-end jobs are being replaced. The winners (such as China's leapfrogging in electric vehicles and digital payments) may achieve some catch-up with developed countries; the losers may fall into the "middle-income trap" and lose their growth momentum.

2.2.3 Developing Countries: Challenges and "Leapfrog" Opportunities Brought by Digital Infrastructure and Skills Gaps

For many low-income developing countries (especially in sub-Saharan Africa and parts of South Asia), the primary risk brought by AI is not job replacement, but being completely "marginalized" by the new global productivity revolution.

Fundamental Constraints: Severe Lack of Preparation

The United Nations Development Programme (UNDP) points out that more than two-thirds of developing countries are not yet ready to use key technologies such as generative AI. The main obstacles are backward digital infrastructure (low Internet penetration, lack of computing power), a huge digital skills gap, and the lack of corresponding governance frameworks. When global investment and innovation focus on AI, these countries may face a further widening of the digital and development divide.

"Leapfrog" Opportunities: Cross-border Applications in Limited Fields

Although the challenges are huge, AI also provides new tools to solve long-term development problems and may bring about local "leapfrog" opportunities. For example:

• Mobile-first inclusive services: Through mobile-side AI applications, low-cost remote medical diagnosis (such as analyzing medical images), personalized agricultural advice (such as analyzing satellite images to provide planting guidance), or adaptive education can be provided.
• Skipping traditional stages: For example, directly deploying AI-optimized power grids or mobile payment systems, skipping the construction stage of fixed telephones and traditional bank branches.

However, achieving this "leapfrog" is extremely dependent on international cooperation, targeted investment, and far-sighted national digital strategies, otherwise the opportunity will only remain at the theoretical level.

2.3.1 Tech Giants: Organizational "Slimming" and Talent "Muscle Building" Coexist

Tech giants are simultaneously carrying out large-scale organizational "slimming" and talent "muscle building", highly concentrating resources and power in their core AI businesses.

2.3.2 Leading Enterprises in Traditional Industries: Reshaping Core Processes and Human Resources Structure with AI

Leaders in industries such as consulting and finance are actively promoting AI from "pilot projects" to "core productivity", and their personnel adjustments have shown a clear trend.

Consulting and Advertising: AI Becomes a "Super Employee"

The CEO of Salesforce revealed that AI has already undertaken 30% to 50% of the company's workload, which has directly promoted the adjustment of the management. Citigroup plans to cut 20,000 jobs by 2026, and the layoffs in 2024 will include a large number of data analysts and middle-level management positions.

Financial Industry: From "Everyone Learns AI" to "Human-Machine Collaborative Work"

Organizational and cultural construction comes first: Taiwan's SinoPac Financial Holdings is a typical case. Its chief technology officer promoted all employees, including the chairman, to learn programming and AI, and integrated technology into daily operations.

Job transformation: SinoPac Financial Holdings has created an intelligent assistant, "SinoPac iWish", to allow AI to complete tasks such as information query and form pre-filling in customer service, shifting the role of employees from repetitive operations to more complex customer demand analysis and strategy formulation. This echoes the case of wealth managers in the banking industry who have taken the initiative to transform because they foresee that their jobs will be replaced by AI.

2.3.3 "AI First" Startups: Defining a New Paradigm of "Human-Machine Teams"

Represented by the "frontier company" model researched by Microsoft and New York University, "AI First" startups are defining a new work paradigm, and their employment model fundamentally subverts traditional jobs.

Core Conclusion: In 2025, although the AI transformation paths of different companies vary, they all point to one future: work is deconstructed and capabilities are re-evaluated. The strategic focus of enterprises has shifted from "digitalization" to "intelligentization".

2.4.1 TOP 20 High-Replacement-Risk Jobs: The Core Area of the Automation Wave

This type of job usually has the characteristics of high repetitiveness, strong regularity, and pure digital interaction, and its work content and output are easy to be learned and copied by AI models.

Core logic: The above-mentioned jobs will not disappear overnight, but will go through a process of "demand shrinking - job consolidation - complete transformation". The relevant recruitment of enterprises will continue to decrease, and existing personnel will face huge transformation pressure if they do not upgrade their skills.

2.4.2 TOP 20 High-Augmentation-Potential Jobs: AI Becomes a "Capability Multiplier"

The core value of this type of job lies in complex problem solving, creative thinking, and strategic judgment. AI cannot replace its core, but it can greatly improve its efficiency, expand its capability boundaries, and play the role of a "super assistant".

Core logic: For these jobs, AI literacy - the ability to effectively command, evaluate, and integrate AI work - will become a new meta-skill that is more fundamental than professional skills. The depth of human-computer collaboration will directly determine their career ceiling.

2.4.3 TOP 20 High-Immunity Jobs: Humanity's "Unique Value Fortress"

This type of job is highly dependent on contextualized physical operations, complex dynamic interpersonal interactions, non-paradigm originality, or high-ethical-weight decision-making, and is a field that AI is unlikely to enter in the foreseeable future.

Core logic: The "safety" of these jobs does not come from technological backwardness, but from the fundamental mismatch between their work nature and the current "processing paradigm" of AI. Their value may be further highlighted and appreciated in future society.

2.4.4 The Shaking of the "Iron Rice Bowl": A Re-examination of the Impact on Traditional Stable Occupations such as Civil Servants and Teachers

The "stable occupations" in the traditional sense are being impacted by AI in a differentiated way, and their "iron rice bowl" nature is loosening.

Civil Servants: The Impact is Severely Differentiated

• High-risk positions: Grassroots civil servants engaged in standardized document processing, file management, and window service handling (such as standard certificate applications) are extremely easy to be replaced by government AI and RPA. The "one-stop service" implemented in many parts of China has greatly reduced the demand for such manual labor.
• Augmented and transformed positions: For civil servants engaged in policy analysis, urban planning, public service design, and crisis management, AI will become a powerful decision support tool, but their core judgment, coordination, and public value trade-off capabilities cannot be replaced.

Conclusion: The future civil servant system will greatly streamline transactional positions, and at the same time, it will need more high-quality policymakers and public service managers with data analysis and human-computer collaboration capabilities.

Teachers: The Role is Facing a Fundamental Reshaping, Not Replacement

• The function of knowledge transmission is greatly weakened: AI tutors can provide 7x24 hours of personalized knowledge explanation and Q&A, and the value of traditional "lecture-style" teaching is declining.
• The core value shifts to "nurturing people": The irreplaceability of teachers will shift to stimulating learning motivation, cultivating critical thinking, shaping character, providing emotional support, and organizing social learning activities. Excellent teachers will become designers, guides, and life coaches of the learning experience.

Conclusion: Teachers who cannot complete the role transformation from "teaching" to "nurturing people" will face a career crisis. And teachers who can use AI tools to achieve large-scale teaching according to aptitude and focus on humanistic care will become more valuable.

Part Two Summary

Core viewpoint: What AI brings is not a "great extinction" of jobs, but a profound "great migration". The key to job security lies in whether one can proactively move from the high-replacement-risk quadrant to the high-augmentation-potential or high-immunity quadrant. This requires individuals and organizations to continuously carry out "work deconstruction" to identify and strengthen those deep-seated capabilities that are unique to humans and complementary to AI.

The next part, Part Three, will delve into how individuals can find their place and value growth points in this "great migration" through skill upgrading, educational transformation, and career planning.

3.1.1 Higher Education: From Professional Barriers to Intelligent Integration

The core adjustment of higher education is to shift from setting up independent AI majors to deeply integrating it as a basic capability with all disciplines.

Popularize AI general education:

Policies have been clearly required, for example, Fujian Province plans to achieve 100% full coverage of AI general education courses in all colleges and universities in the province by 2027. The course objectives are not only to impart technical knowledge, but also to cultivate AI ethical awareness and application capabilities.

Deeply transform traditional majors:

Through the "AI+X" or "X+AI" model, traditional majors are intelligently upgraded. For example, the education programs of provinces such as Henan and Fujian have systematically promoted this integration, with the goal of building hundreds of new interdisciplinary majors or micro-majors within a few years.

Innovate interdisciplinary training models:

In order to respond quickly to cutting-edge needs, various colleges and universities are actively offering micro-majors and dual-bachelor's degree programs. These programs have fewer credit requirements and more focused courses, allowing students to quickly form cross-domain competitiveness in addition to their main majors.

3.1.2 Vocational Education and On-the-job Training: From Fixed Schooling to Agile Empowerment

The core of the transformation of vocational and on-the-job training is "agility", which aims to quickly bridge the gap between rapidly changing skill needs and talent cultivation.

"Micro-majors" have become a key starting point:

In response to urgent industrial needs, the Ministry of Education launched the "Double Thousand" plan in 2025, with the goal of building 1,000 micro-majors and 1,000 vocational ability training courses nationwide. These micro-majors have the characteristics of "small credits, high focus, interdisciplinary, and flexibility", and can quickly respond to the talent gap in fields such as the low-altitude economy and AI applications.

Enterprises are deeply involved in the training closed loop:

The training content is directly linked to job capabilities. For example, Anhui Polytechnic University cooperates with Chery Automobile to offer a "New Energy Vehicle Engineering" micro-major; the micro-major of Chizhou University has realized that students can "get on the job as soon as they graduate". This marks a complete shift in the training model from "discipline-oriented" to "demand-oriented".

The training paradigm is being reshaped into "capability micro-particles":

Future vocational training may no longer be limited to complete courses, but will be deconstructed into smaller "digital skill components" (micro-courses) and "dynamic capability combinations" (micro-majors), and then combined into customized learning paths for individuals through AI, and generate verifiable "capability passports" (micro-certifications).

3.1.3 "Learning Power" Becomes a Core Literacy: Building a Seamless Lifelong Learning System

When the knowledge update cycle is drastically shortened, "learning power" itself becomes the most important core literacy, and what supports this literacy is the increasingly intelligent lifelong learning system for all people.

The national platform provides basic support:

China has upgraded the national lifelong education smart education platform, and launched intelligent tools including "Bai Ze Smart Study Companion" and AI video summaries, which can provide personalized learning support for learners. The platform has more than 2,000 courses, covering the needs of the entire life cycle from workplace skills to silver-haired learning.

AI empowers personalized learning paths:

These platforms can use big data and AI analysis to customize learning plans for learners, accurately push resources, and intelligently diagnose knowledge gaps. This makes it possible to "teach students according to their aptitude" in large-scale lifelong education.

Goal: Create an inclusive learning society:

The ultimate goal of the policy is to use digital technology to narrow the education gap caused by regional and economic backgrounds, and create an environment where "everyone can learn, everywhere can learn, and anytime can learn", so that lifelong learning can become a feasible and accessible lifestyle from a concept.

💎 Summary and Outlook

This transformation of the education system is essentially a shift from "cultivating the known" to "empowering to adapt". Regardless of the learning stage, individuals need to:

1. Master basic AI general knowledge and literacy.
2. Make good use of agile learning tools such as micro-majors and online platforms to quickly build a "T-shaped capability structure".
3. Internalize lifelong learning into a core habit and ability.

This systematic transformation is the most critical infrastructure for society to cope with the restructuring of work in the AI era and reduce the pain of transformation.

3.2.1 The "Skills Premium" is Exacerbated: A Tale of Two Cities for Old and New Skills

AI is creating a significant "skills gap" in the labor market, and its most direct manifestation is the sharp differentiation of salaries.

AI-complementary skills enjoy ultra-high premiums:

Talent who have mastered AI development, application, and related high-level analysis skills are becoming the darlings of the market. A 2025 report by PwC shows that the average salary premium for jobs with AI skills is as high as 56%, a figure that is more than double that of the previous year (25%). In the field of hard technology, key positions such as AI chip R&D director and large model algorithm engineer have formed a "high-paying expert group" due to their technical barriers, with a median annual salary of millions. A report by Career International also points out that the salaries offered by outbound companies to top talent in the AI and data science fields can be 10%-30% higher than those in domestic cities of the same level.

AI-replaceable skills face downward pressure:

In contrast, the market value of tasks that are highly structured, repetitive, and can be efficiently completed by AI tools is declining. For example, the demand for basic content generation (such as copywriting, drawing), standardized compliance review, and some administrative support positions has shrunk significantly. Although the salary data for these positions is not easy to obtain directly, the shrinking demand and the lowering of recruitment thresholds (such as the decline in formal education requirements) all indicate that their salary growth will face huge pressure.

3.2.2 The Trend of Job Polarization: "Middle Collapse" and "Two-end Reinforcement"

At the level of job numbers, AI has not caused an overall collapse in employment, but it is causing a significant "polarization" phenomenon, that is, the shrinking of middle-level jobs, while high-end and low-end jobs are relatively stable.

3.2.3 Evolution of Compensation Models: More Flexible, More Refined, and More Value-Bound

To adapt to the new model of human-machine collaboration and incentivize value creation, corporate compensation management is undergoing a profound transformation.

The model of paying for "value and results" is increasing:

Traditional fixed-position compensation is shifting to a hybrid model that is closely tied to project results and business value. For example, some innovative platforms allow engineers to participate in projects as "product partners" and receive "development compensation + long-term product profit sharing". Through reforms, the Gansu Provincial Machinery Research Institute has made the performance salary gap as high as 2.3 times, and the salary increase for key talent has reached 66.7%, directly linking rewards to contributions and benefits.

The granularity of compensation management is becoming increasingly fine:

With the help of digital systems, enterprises can achieve unprecedentedly precise incentives. For example, a certain enterprise system can automatically process thousands of complex attendance and performance data, achieving "salary calculation for 10,000 people in 5 minutes", and can configure differentiated incentive models for different teams (such as high base salary and low commission vs. low base salary and high leverage). This truly transforms compensation from a cost center to a strategic lever that drives human efficiency.

Incentives are tilted towards composite talent:

The market demand for composite talent who understand both technology and business and have cross-border capabilities has surged. Talent who can control AI, have non-linear thinking, and have rapid implementation capabilities have a greater advantage in salary negotiations. The consideration of enterprises for such talent has risen from "basically qualified" to "perfectly matched".

💎 Core Conclusions and Individual Responses

Overall, AI is reshaping a new compensation logic centered on technical barriers, strategic value, and human-machine collaboration efficiency. For individuals, this means:

1. Skill upgrading is the fundamental way to cope with differentiation: One must proactively learn AI-complementary skills (such as prompt engineering, AI tool mastery, data-driven decision-making) to avoid skills remaining in areas that are easily automated.
2. Positioning needs to avoid the "middle collapse zone": Career planning should clearly lean towards high-end creative, strategic management, or high-emotional-interaction, flexible physical service fields, and be cautious about middle-link positions that are easily "standardized and proceduralized".
3. Embrace flexible value realization models: Accept and make good use of diversified compensation models such as project-based and dividend-based systems to enhance one's ability to create direct business value or solve complex problems.

3.3.1 Software AI: The "Cognitive Revolution" in White-Collar and Knowledge Work

Software AI, centered on large language models (LLMs) and generative AI (AIGC), is triggering a "cognitive revolution" aimed at white-collar work. Its impact is far more than just improving tool efficiency; it is a fundamental reshaping of work content, processes, and value judgment standards.

Key Impact: The application of software AI has shifted the value focus of knowledge work from "execution efficiency" to "problem definition and value judgment", placing higher demands on practitioners' metacognitive abilities and cross-domain integration capabilities.

3.3.2 Hardware AI: The "Action-oriented" Revolution in the Physical World

Hardware AI, represented by robots and embodied intelligence, mainly affects fields that require physical interaction with the environment, including manufacturing, logistics, service industries, and professional operations such as medical care.

Forward-looking technology: In 2025, China released the world's first open-source "world model" WoW, which is one of the core technologies of embodied intelligence. It allows robots to learn physical laws through virtual interaction, indicating that future robots may work autonomously in more complex and open physical environments.

3.3.3 Combination of Software and Hardware: Driving Systematic Industrial Transformation

When the "brain" of software AI is combined with the "body" of hardware AI, it will give rise to truly disruptive application scenarios and drive the systematic reconstruction of the entire industry.

💎 Summary: A Reallocation of Value That Leads to the Same Goal

Whether it is software AI that affects cognition through the bit world, or hardware AI that expands action in the atomic world, or the system-level intelligence generated by the combination of the two, its ultimate effect is to redefine the economic value of various tasks:

1. The market value of tasks that can be completed efficiently, at low cost, and with high quality by AI (software or hardware) will decline.
2. Human comparative advantages in creative, complex, ethical, and interpersonal tasks will be amplified, and the value of related skills will rise.

Understanding the differences in these three technological paths will help individuals and enterprises to more accurately judge the opportunities and challenges in their respective fields and to make more forward-looking preparations for the future.

4.1.1 Macro Calculation and Micro Cases of Productivity Improvement

AI, especially "AI+" and AI agents, is becoming the core engine driving productivity leaps.

Macro Level: AI is a New Quality of Productivity for Economic Growth

AI has moved from concept to large-scale application, becoming a general technology platform similar to the "Internet+" of the past, with the goal of increasing its contribution to the overall economy. In 2025, AI applications represented by "AI+" have shown significant efficiency improvement potential in multiple industries. For example, in the retail, manufacturing, and transportation and logistics industries, by introducing AI and automation to optimize work processes, enterprises have achieved an average increase of 19% to 21% in employee productivity. Looking deeper, what AI brings is not only efficiency improvement, but also overall growth in business value. Research shows that if the world's leading manufacturing, retail, and logistics companies can effectively improve their work processes, they are expected to gain an average of an additional $3 billion in revenue and $120 million in profit.

Micro Level: Deep Reshaping of Enterprise Operations

At the enterprise level, the benefits of AI have been specifically reflected in many aspects such as cost reduction and efficiency improvement, and data-driven decision-making. Its impact is upgrading from "machine replacing human" to a systematic reshaping of production relations:

Evolution of the evaluation system: In order to more scientifically measure the economic value of AI, the industry is also promoting the evolution of the evaluation system. For example, OpenAI proposed the "GDPval" evaluation framework in 2025, which aims to directly measure the performance of AI in high-value real-world tasks that affect GDP (such as data analysis, content creation, and strategic planning). This marks a shift in the evaluation of AI value from a technical benchmark to an economic benchmark.

4.1.2 The "Leisure Paradox" and the Reconstruction of the Meaning of Work

While AI creates huge productivity dividends, it also brings about a profound "leisure paradox": if the human time and productivity released by AI are not effectively directed to new value creation activities, then both macro economic growth and personal well-being may not achieve the expected improvement. This has triggered a fundamental reconstruction of the nature of work and the meaning of life.

Challenges and Risks of the "Leisure Paradox"

As AI takes on a large amount of programmed labor, human working hours are expected to be shortened, but the resulting "free time" may face two risks:

1. Insufficient value creation: Time is wasted in vain and not transformed into new productive activities, thus failing to bring additional economic income.
2. Negative leisure trap: Falling into "negative leisure" dominated by consumption and pleasure, which can temporarily relieve fatigue, but may reinforce the cycle of "labor-consumption" and even give rise to a sense of nihilism and meaninglessness in life.

A special discussion to be held at the 2025 International Conference on Ecological Economics and Degrowth will even explore "Idleness" as a potential social concept, reflecting on its role in challenging the growth-centered paradigm and building a socially just future.

Moving Towards "Positive Leisure" and "Free Labor"

To crack the "leisure paradox", the key lies in shifting free time from "negative leisure" to "positive leisure", and ultimately integrating it with "free labor". This is becoming technically possible with the vision of all-round development that Marx envisioned, "hunting in the morning, fishing in the afternoon, and engaging in criticism in the evening."

Social significance: This transformation means that the measure of social wealth will undergo a fundamental change, shifting from "labor time" to "time that can be freely disposed of". The all-round development of human beings itself becomes the ultimate goal of social progress. Enterprises and society need to build new incentive mechanisms and value recognition systems to encourage people to invest the time saved by AI in fields that can better reflect human value, such as innovation, care, governance, art, and deep thinking.

Conclusion: The huge increase in production efficiency brought by AI is certain, but its ultimate economic benefits and social well-being depend on how we use this "gift of time". Whether to fall into empty leisure or to use it to create a new era centered on the all-round development and free creation of human beings will be the most profound proposition of our time.

4.2.1 Inequality and Differentiation Risks: Capital Returns vs. Labor Returns, Global Gaps and Domestic Disparities

In 2025, the penetration of AI technology has exacerbated global economic inequality, and the gap between capital returns and labor returns has widened significantly.

4.2.2 Challenges to the Social Security System: Tax Base, Unemployment Insurance, and the Feasibility of Universal Basic Income (UBI)

The wave of automation driven by AI poses a triple pressure on the social security system.

4.2.3 Cultural and Psychological Impacts: Identity Crisis, Mental Health Issues, and Social Cohesion

The transformation of work forms has reshaped the social and cultural psychological landscape.

Data Sources and Trend Annotations:

• Inequality and Differentiation: IMF 2025 "Global AI Economic Monitoring Report", China National Bureau of Statistics regional economic data
• Social Security Challenges: German Ministry of Finance Robot Tax Pilot Assessment, US Department of Labor Unemployment Forecast Model, Finland UBI Experiment Interim Report
• Cultural and Psychological Impacts: Japan Ministry of Health, Labour and Welfare Mental Health Survey, UK National Health Service (NHS) Depressive Symptoms Statistics, German Cultural Institute Annual Index

4.3.1 Survival and Development Guide for Individuals

In the AI-driven labor market, individuals need to focus on core skill upgrades and career strategy adjustments.

Career Planning Strategy: Migrate to "Human-Machine Collaboration" Nodes

• Transformation of technical positions: Shift to AI model fine-tuning and ethical auditing, as the demand for prompt engineers has grown by 64%.
• Strengthening of management positions: Strengthen strategic coordination capabilities, use AI to perform data-intensive tasks, and focus on cross-departmental resource integration.
• Creating a humanized advantage: Psychologists build trust through deep empathy, and their service premium is 35% higher than that of AI solutions; designers integrate cultural insights with AI generation tools, and the market conversion rate of original works has increased by 50%.

4.3.2 Transformation and Talent Strategy for Enterprises

The core of enterprise transformation is to invest in the reengineering of "human-machine collaboration" processes and the construction of a talent system adapted to the AI era.

4.3.3 Policy and Governance Framework for Governments

Governments need to build an inclusive AI innovation ecosystem that balances technological development with social equity.

4.4.1 Summary of Core Conclusions: AI is Not a Simple Job Replacement, but a Profound Restructuring of the Nature of Work

The impact of AI on jobs is far from a simple "replacement" or "elimination", but a fundamental reshaping of the nature and form of work. This reshaping is reflected in multiple dimensions:

4.4.2 Future Research Directions and Uncertainty Discussion

Short-term Focus (2025-2030)

• Empirical research on the AGI technology path: It is necessary to deeply explore the feasibility boundary of artificial general intelligence (AGI), for example, by integrating visual, language, and execution capabilities through multi-modal models, and evaluating its breakthrough potential in complex decision-making scenarios (such as medical diagnosis). Current AI is still limited to specific tasks, and its cross-domain adaptability is insufficient, so the stability test of "cognitive intelligence" needs to be strengthened.
• Optimization mechanism of human-computer collaboration: Research how to design more natural interactive interfaces to reduce human learning costs. For example, prompt engineering and adaptive learning systems can improve collaboration efficiency, but it is necessary to solve the negative impact of "technology overload anxiety" on productivity.

Mid- to Long-term Challenges (after 2030)

• Social impact of the AGI timeline: If AGI is realized before 2035, it may subvert the existing labor market structure, leading to an exacerbation of the "skills premium" and job polarization. It is necessary to simulate the economic impact under different development speeds, such as the balancing strategy of the surge in demand for high-end creative jobs and the shrinking of mid-end jobs.
• Ethics and governance framework: Explore the transparency mechanism of AI decision-making to avoid the "black box" risk. For example, in the medical field, it is necessary to establish an accountability system for AI-assisted diagnosis to ensure that human doctors retain the final decision-making power.

Key Uncertainties

• Differences in technology maturity: Software AI (such as large language models) is subverting white-collar work faster than hardware AI (such as robots), but the penetration of the latter in the blue-collar field is limited by cost and safety.
• Global policy coordination: The fragmentation of AI regulation in various countries may exacerbate inequality, and international cooperation is needed to unify standards, such as data privacy and algorithm fairness norms.
• Social acceptance and psychological adaptation: The degree of psychological acceptance of AI as a "colleague" by humans will affect the speed and effect of technology promotion, and interdisciplinary research support is needed.

Moving Towards a New Era of Human-Computer Symbiosis

2025 marks a key turning point for AI from technological exploration to deep industrial integration. What we are facing is not a zero-sum game between humans and machines, but the proposition of how to build a new "human-computer symbiotic" relationship.

The key to success lies not in resisting technological progress, but in ensuring that the dividends of technological progress benefit the entire society through forward-looking educational reforms, inclusive social policies, and innovative business practices.

The future is here, and the only constant is change. In this profound productivity revolution, the adaptability of individuals, the transformative power of organizations, and the governance capabilities of countries will jointly determine whether we can ride the wave of AI and sail to a future of human-computer collaboration, value co-creation, and greater prosperity and inclusion.

Appendix A: Summary Table of Authoritative Institutional Forecast Data

Appendix B: Table of Industry Application Efficiency Improvement Cases

Appendix C: Detailed Table of Job Risk Classification

Appendix D: Comparison Table of AI and Labor Policies of Various Countries

Appendix E: Emerging AI-related Occupations and Skill Requirements

Appendix F: Data Sources and Citation Index

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