Introduction: Analysis of AI's Impact on the Global Labor Market (2020-2024)

This study is based on 2020-2024 data and analyzes AI's impact on the global employment landscape, salary structure, remote work models, and industry transformation. The original author is gibiai, published on GitHub (2026-05-27), aiming to answer key questions such as AI's impact on salaries across different experience levels, the correlation between remote work and AI, and the varying impacts on different industries.

Research Background and Motivation

From 2020 to 2024, artificial intelligence technology moved from laboratories to large-scale commercial applications, and combined with the post-pandemic remote work wave, making its impact on the labor market more complex. This study attempts to answer: What is AI's impact on the salaries of practitioners with different experience levels? Is there a correlation between remote work and AI applications? Which industries are most vulnerable to AI automation shocks?

Dataset and Research Methods

A comprehensive dataset covering 2020-2024 was built, integrating multiple public data sources. A multi-dimensional analysis approach was adopted: salary trend tracking, correlation analysis of remote work distribution, research on experience level differences, and industry segmentation comparison (technology/finance/manufacturing/services, etc.). The project includes data processing, analysis scripts, and visualization resources to facilitate reproduction and expansion.

Key Findings: Salaries, Remote Work, and Changes in Industry Experience

• Salary Structure: AI leads to a stratification effect—salary premiums for high-skill positions expand, while salaries for mid-to-low-end repetitive positions stagnate or decline (skill-biased transformation); those who master AI tools have higher productivity and salaries.
• Remote Work: Positively correlated with AI applications. AI collaboration tools improve the efficiency of distributed teams, reducing the importance of geographical factors, while skill matching becomes more critical.
• Industry Differences: The technology industry undergoes 'creative destruction' (replacing traditional positions + spawning new occupations); the finance industry sees gradual changes (AI-assisted risk assessment, etc.); manufacturing directly impacts frontline workers.
• Experience Levels: Polarization—AI lowers the entry threshold for some tasks, but advanced decision-making capabilities become more scarce.

Policy and Education Implications and Recommendations

• Policy: Upgrade retraining programs to forward-looking skill transformation initiatives; adjust social security systems to cover non-standard employment;
• Education: Incorporate AI literacy into core curricula from basic education to higher education, and cultivate the ability to collaborate with AI.

Future Outlook and Conclusion

After 2024, large language models/multimodal AI will usher in new transformations. This study provides baseline data but requires continuous tracking. Individuals need to develop 'AI-augmented skills', and enterprises need to deploy AI responsibly. AI's impact on the labor market is a complex restructuring rather than a simple replacement; proactive adaptation, continuous learning, and policy innovation are the ways to respond.