Analysis of Demand Changes for AI Annotators and Value Migration in the Industry Chain

Based on the latest market research and industry data analysis, the demand changes for AI annotators deeply reveal that the AI industry chain is undergoing major value migration. Here is the detailed analysis:

From 2022 to 2025, the AI annotation industry has shown a clear shrinking trend. Annotators’ salaries have continued to decline, data annotation demand has decreased, and the market size has gradually contracted [1]. This phenomenon reflects that the industry is moving from “uncontrolled growth” to a rational adjustment period.

Traditional “entry-level annotation” positions are being eliminated, replaced by higher-tech AI trainer roles. Taking Yijun County, Shaanxi Province as an example, local AI trainers need to pass strict assessments: “switching between 7 tasks in one day at most and taking 7 exams”, and are required to work with zero errors [2]. This indicates that basic annotation work is being replaced by automated tools, and the value chain is migrating to high-end areas.

: Relying on a large number of manual workers for data cleaning and annotation
: Popularization of automated annotation tools and AI-assisted annotation systems
: Efficiency improved by 10-100 times, labor shifted to quality control and complex scenario processing

According to KPMG’s Artificial Intelligence Readiness White Paper, 73% of enterprises need data annotation and cleaning teams, while also requiring the establishment of a full-time AI governance committee and cross-departmental agile collaboration processes [3]. This means:

• Data management platform
  value is prominent
• Workflow efficiency
  becomes core competitiveness
• AI engineering capability
  becomes a key barrier

Market demand changes are reflected in salary structures:

• Entry-level programming positions decreased by 27% due to generative AI
• Demand for high-skilled positions such as cybersecurity and AI ethics increased by 68% [4]
• High-value talent characteristics
  : Engineering capability, model control, cross-border perspective

National policy orientation is clear:

• Established a 60.06 billion yuan National Artificial Intelligence Industry Investment Fund in January 2025
• Focus on AI computing power construction and application scenario development
• Emphasize the construction of a full-factor policy system for
  data, computing power, and algorithms
  [3]

Demand for professional annotation in vertical fields has grown against the trend:

• Medical AI annotation
  : For example, iFlytek Medical AI medical researchers need to have professional medical knowledge
• Autonomous driving annotation
  : High-precision scene understanding capability
• Financial risk control annotation
  : Extremely high requirements for compliance and accuracy

1. Toolchain evolution
   : MLOps platforms and automated tools have greatly improved annotation efficiency
2. Enhanced model capability
   : AI systems can perform self-correction and annotation to a certain extent
3. Cost pressure
   : Enterprises pursue higher ROI and require more lean data processing processes

1. Capital withdrawal
   : The market shifts from “building models” to “building applications” [5]
2. Accelerated application landing
   : China released application lists for scenarios such as drug supervision, higher education, and new industrialization in 2024
3. Stricter supervision
   : Need to establish an AI ethics review system and hierarchical supervision system

1. Capability mismatch
   : Academic talents are difficult to adapt to engineering landing needs
2. Value re-evaluation
   : The top 1% of scientists and talents with cross-border capabilities are prominent in value
3. Lifelong learning
   : AI capability gradually becomes a standard for technical positions [5]

1. Data management platform
   : Integrated solution for annotation, cleaning, and quality inspection
2. Vertical field AI training
   : Professional scenarios such as medical care, finance, and law
3. AI governance and compliance
   : Ethical review, risk control
4. Human-machine collaboration system
   : Optimal combination of manual judgment and AI efficiency

New first-tier cities are rising rapidly:

• Hangzhou and Nanjing account for 4.38% and 4.22% respectively
• Xi’an, Jinan, Wuhan and other cities account for more than 3%
• Policy support continues to increase [6]

For practitioners, they need:

• Upgrade from “single annotation” to “full-process understanding”
• Master professional knowledge in at least one vertical field
• Cultivate engineering thinking and problem-solving capabilities
• Pay attention to AI ethics and compliance frameworks

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[1] CSDN - AI Large Model Talent Market Shuffle: From “High Salary Low Efficiency” to “Value Creation” in 2025 (https://blog.csdn.net/EnjoyEDU/article/details/155483704)

[2] People’s Daily - AI Trainer: Making Artificial Intelligence More “Understanding” of Humans (http://paper.people.com.cn/rmrbhwb/pc/content/202509/26/content_30107195.html)

[3] KPMG - Artificial Intelligence Readiness White Paper (https://assets.kpmg.com/content/dam/kpmg/cn/pdf/zh/2025/06/artificial-intelligence-readiness-white-paper.pdf)

[4] Beijing Academy of Artificial Intelligence - Artificial Intelligence Brings “Unemployment Wave” to the U.S. (https://hub.baai.ac.cn/view/44272)

[5] Everyone is a Product Manager - AI Product Manager Detailed Explanation: 2025 New Trends (https://www.woshipm.com/pmd/6245924.html)

[6] 2024 China Artificial Intelligence Job Recruitment Research Report (https://pdf.dfcfw.com/pdf/H3_AP202501091641865653_1.pdf)