The monthly income of AI large model data annotation workers has dropped to no more than 5,000 yuan, and the unit price of annotation has dropped from 50 cents to 4 cents.

News on October 9th, AI large models have been a hot topic in the field of artificial intelligence in recent years. They can achieve various amazing functions, such as generating Realistic text and images, or have smooth conversations with humans. But behind these large models, there is a group of unknown data annotators who work hard every day to add labels to raw data and provide AI technology with the massive data required for training.

The work of data annotators is not easy. They need to face boring tasks, low income, long-term instability and the risk of being replaced at any time. They are the cornerstone of the development of AI technology, but receive little attention and respect.

According to "Tech Planet", data annotators use the most primitive piece-rate system to calculate their wages. Most practitioners earn no more than 5,000 yuan per month. Some of them are college graduates, some are mothers, and some are career changers. They process pictures, text, voice and other data in cubicles in third- and fourth-tier cities, providing services to major Internet companies and car companies.

This site has noticed that the data annotation industry has also experienced ups and downs. In 2017, when expectations for AI technology were booming, data annotators could earn high incomes, with a 2D pull box earning 50 cents. However, as competition in the industry intensifies and technology development does not go smoothly, the unit price of data annotation is getting lower and lower, and now the lowest is only 4 cents.

Data annotation companies are also facing tremendous pressure. They need to have a certain scale and capital reserves to obtain orders from the source, and they have to bear problems such as long payment cycles, high employee turnover, and unstable quality and cycle. Haitian Ruisheng is currently the first main board-listed company in the data annotation industry. Its profit margin last year was just over 10%, and it fell into losses in the first half of this year.

What worries data annotators is that they may soon be replaced by the AI ​​they helped create. Some companies at home and abroad are developing tools that can automatically label data, using mainstream large models on the market to label data sets. These tools claim to improve labeling efficiency and reduce costs, and achieve an accuracy that is close to or even exceeds that of manual labor

Of course, not all data labeling can be replaced by AI. Some data annotations that require professional knowledge and logical analysis capabilities still require manual participation, such as medical, financial, autonomous driving and other fields. But this also means that the threshold of the industry will continue to increase. For data annotators, if they want to survive in this industry, they may need more learning and hard work

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