To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

The question of whether AI substitutes or complements human work is central to debates on the future of work. This paper examines the impact of AI on skill demand and compensation in the U.S. economy, analysing 12 million online job vacancies from 2018 to 2023. It investigates internal effects (within-job substitution and complementation) and external effects (across occupations, industries, and regions). Our findings reveal a significant increase in demand for AI-complementary skills, such as digital literacy, teamwork, and resilience, alongside rising wage premiums for these skills in AI roles like Data Scientist. Conversely, substitute skills, including customer service and text review, have declined in both demand and value within AI-related positions. Examining external effects, we find a notable rise in demand for complementary skills in non-AI roles linked to the growth of AI-related jobs in specific industries or regions. At the same time, there is a moderate decline in non-AI roles requiring substitute skills. Overall, AI's complementary effect is up to 50% larger than its substitution effect, resulting in net positive demand for skills. These results, replicated for the UK and Australia, highlight AI's transformative impact on workforce skill requirements. They suggest reskilling efforts should prioritise not only technical AI skills but also complementary skills like ethics and digital literacy.

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023, this dataset encompasses monthly recruitment demand for 2,324 types of skills across 521 companies. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research.

The rapidly changing landscape of technology and industries leads to dynamic skill requirements, making it crucial for employees and employers to anticipate such shifts to maintain a competitive edge in the labor market. Existing efforts in this area either rely on domain-expert knowledge or regarding skill evolution as a simplified time series forecasting problem. However, both approaches overlook the sophisticated relationships among different skills and the inner-connection between skill demand and supply variations. In this paper, we propose a Cross-view Hierarchical Graph learning Hypernetwork (CHGH) framework for joint skill demand-supply prediction. Specifically, CHGH is an encoder-decoder network consisting of i) a cross-view graph encoder to capture the interconnection between skill demand and supply, ii) a hierarchical graph encoder to model the co-evolution of skills from a cluster-wise perspective, and iii) a conditional hyper-decoder to jointly predict demand and supply variations by incorporating historical demand-supply gaps. Extensive experiments on three real-world datasets demonstrate the superiority of the proposed framework compared to seven baselines and the effectiveness of the three modules.

Today's digital world has created a booming demand for new skills, including the technical knowledge to develop artificial intelligence (AI) tools as well as the aptitude to apply and use AI in the workplace. But a new survey of higher education officials suggests that demand for AI training is outpacing supply and the current ability of higher education institutions to meet that demand. The study, which polled 246 prequalified higher education administrators, educators and IT decision makers from a mix of community colleges, four-year colleges and vocational schools, also suggests that while higher education officials recognize the growing demand for AI instruction, 52% of them say they are struggling to attract instructors to teach AI courses. One reason is that the demand for AI subject matter experts -- and what companies are willing to pay them -- is so high in the commercial sector that schools are having a hard time competing for talent. But the study, conducted in April/May 2021 by EdScoop and underwritten by Dell Technologies and Intel, also found college officials face a variety of other challenges.

When robotics and the concept of automation first caught the public's attention, the common consensus was that the manufacturing industry would be the testing ground for the new technology. This proved true, but for more advanced systems – AI, machine learning, blockchain and advanced analytics – the financial services sector is the new Ground Zero.

New technologies tend to shift jobs and skills. New technologies bring new products, which shift jobs across occupations. With the arrival of cars, the economy needed more assembly line workers and fewer blacksmiths. New technologies also bring new work processes, which shift skills in jobs. With the arrival of copiers, office workers needed to replace ink cartridges but not use carbon paper.