Is AI bringing about a surge in joblessness or efficiency full circle?
Undoubtedly, AI has become a key driver of industrial innovation. Pioneering enterprises in thousands of industries have found practical applications in areas such as data analysis, intelligent dialogue, marketing, and office automation. At the same time, we can observe that generative AI technology is transforming every aspect of software development, including the underlying operating system, database, coding, testing, project management, and maintenance. These processes are now deeply integrated with intelligent capabilities. One may wonder if AI is increasing efficiency or causing unemployment.
Will AI completely take over the labeling process, resulting in manual labeling being laid off?
Due to the high cost and large scale of labelled data required for AI development, a labour-intensive data labelling industry chain specifically for AI has emerged. However, some enterprises have started to adopt AI for data labelling. AI can significantly increase the speed and scale of data processing, reduce human error, and ensure the consistency and accuracy of data labelling. Reducing operational costs and freeing up human resources for higher-value creative work are some of the benefits of AI.
However, completely replacing manual labeling is still a challenge due to the difficulty of accurately identifying and labeling complex scenes and nuances with automated algorithms. Manual judgment and adjustment are still required in such cases. Over-reliance on AI may also lead to the loss of skills among data labeling personnel.
Will smart coding become a must-have tool for developers?
Intelligent coding tools can generate code quickly, reducing the time spent on manual code writing and accelerating project progress. These tools can also detect potential errors and irregular code in real-time, improving overall code quality. Additionally, they can provide optimization suggestions to help developers write more efficient and maintainable code.
However, the use of smart coding tools may have some potential drawbacks, such as over-reliance leading to the degradation of developers' programming skills, privacy and security concerns, cost burdens associated with purchasing and maintaining the tools, accessibility limitations due to different programming languages and platforms, and the possibility that AI may not be able to deal with problems that require deep creativity and human intuition.
Will the digital workforce gradually enter major companies and positions?
Digital workers are virtual workers enabled by AI and automation technologies. They can work 24/7 without time or location constraints, handle highly repetitive and standardized process tasks, and reduce human error. This increases efficiency and responsiveness, frees up human resources, and reduces operating costs for businesses.
However, the use of digital workforce may raise concerns about employment security, as employees may be vulnerable to replacement by automation technology, leading to unemployment. Additionally, the digital workforce has limitations in terms of innovation and personalized decision-making. Introducing a digital workforce also requires consideration of initial investment and technology maintenance costs, as well as the accompanying challenges of data security and privacy protection.
Will testing, operations and maintenance be fully intelligent?
Comprehensive intelligence can significantly improve testing and operations and maintenance (O&M) efficiency and accuracy, reducing human error, improving system stability, and enhancing enterprise competitiveness through automation technologies that enable real-time monitoring, rapid defect repair, and predictive maintenance. However, achieving full intelligence in testing and O&M may require a high initial investment and may weaken critical manual skills, introduce new security risks, and encounter technical failures and compatibility issues during the transition. Therefore, careful planning is necessary to balance the technological benefits and management challenges.
