Why Data Labeling Splits Into Four Levels and Experts Can Earn ¥100–¥1000 per Task

In 2026, AI pre‑annotation can already detect vehicles, pedestrians, and traffic lights in street‑view images with over 95% accuracy, reducing a typical labeling task from five minutes to one minute as annotators only need to adjust boundaries and add missed objects.

Industry analysts (iResearch) estimate that by 2025 roughly 70% of basic labeling work will be partially or fully replaced by AI, rising to over 80% in 2026, while demand for entry‑level labeling positions has dropped more than 40% year‑over‑year and salaries are capped at ¥4,500–¥6,000 per month.

The article clarifies that low‑skill “box‑drawing” annotators will be displaced, but those who can judge AI outputs will become more valuable. It outlines a four‑level hierarchy:

L1 Data Executor : passive order‑taking, manual box‑drawing – being eliminated by AI pre‑annotation and synthetic data.

L2 AI‑Assisted Annotator : uses intelligent tools to verify pre‑labels and focus on difficult samples – the current survival path.

L3 AI Trainer / Quality‑Check Expert : designs labeling rules, analyses error patterns, performs RLHF preference judgments, and reviews outputs in domains such as medical, legal, and finance; monthly salaries range from ¥15,000 to ¥40,000.

L4 Data Strategist : defines “good data”, plans data flywheels, and guides model direction.

OpenAI disclosed in 2025 that training ChatGPT involved more than 500,000 human preference annotations performed by people with programming, writing, and domain expertise, with top RLHF experts earning up to ¥500,000 annually. Reports from Chinese research institutes echo this shift toward knowledge‑intensive labeling.

TalentsAI positions itself at the L3/L4 layer, offering a specialist‑level data platform for large‑model development rather than a traditional crowdsourced labeling service. Its model pays experts ¥100–¥500 per hour, with per‑item compensation of ¥100–¥200, settled weekly. The platform operates remotely with asynchronous collaboration, a five‑level expert certification system, triangular cross‑validation, reasoning‑chain verification, and expert peer review. Over 5,000 PhD‑level contributors and collaborations with more than ten top AI labs are claimed.

Typical tasks on TalentsAI differ from simple box‑drawing and include programming challenges, clinical guideline verification, legal compliance checks, financial risk assessments, and complex scientific reasoning—tasks that require professional background and cannot be handled by generic crowd workers.

Given the rapid improvement of AI pre‑annotation, the longevity of L1 roles is measured in years, not decades. The article advises annotators to upskill: transition to AI‑assisted tools (e.g., Labelbox, CVAT, TalentsAI) to move to L2, acquire domain expertise to aim for L3, or first study a vertical field before entering as a “validator” rather than a “box‑drawer”.

TalentsAI’s public invitation link (https://g.talents-ai.com/s/rFYP) and registration process are provided for interested professionals, along with free learning resources and a certification workflow.