AI may be denting computer science graduates’ job prospects, UK data shows

Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-09-12T07:14:41.860Z • Permanent link

A recent report from the UK's Office for National Statistics (ONS) has shed light on the impact of artificial intelligence (AI) on computer science graduates' job prospects. The data, which covers the period from 2020 to 2022, reveals that the demand for computer science graduates in well-paid roles in finance has declined significantly. This trend is attributed to the increasing automation of tasks, which has reduced the need for human intervention in many financial processes. For instance, the use of machine learning algorithms has enabled banks to automate tasks such as risk assessment and portfolio management, thereby reducing the need for skilled professionals.

According to the ONS report, the decline in demand for computer science graduates in finance has been most pronounced in the UK, where the number of graduates taking up jobs in the sector has fallen by over 20% in the past two years. This trend is expected to continue, with many experts predicting that the use of AI will further reduce the demand for human labor in the financial sector. The impact of this trend is already being felt, with many computer science graduates facing increased competition for jobs in the sector. For example, the job market analytics firm, Glassdoor, has reported a significant increase in the number of computer science graduates applying for jobs in the finance sector, but also an equally significant increase in the number of candidates being rejected.

The decline in demand for computer science graduates in finance is not limited to the UK. Similar trends have been observed in other countries, including the United States and Australia. The use of AI has become increasingly prevalent in the financial sector, with many banks and financial institutions investing heavily in AI-powered technologies. For instance, the investment firm, Goldman Sachs, has announced plans to invest over $1 billion in AI-powered technologies, including machine learning algorithms and natural language processing. The impact of this trend is expected to be felt across the globe, with many experts predicting that the use of AI will further reduce the demand for human labor in the financial sector.

The decline in demand for computer science graduates in finance has significant implications for the data sources domain. The decline in demand for skilled professionals in the sector is expected to reduce the availability of talent, thereby reducing the quality of data that is available to researchers and analysts. This trend is particularly significant for industries that rely heavily on data, such as finance and healthcare. For instance, the use of AI-powered technologies has enabled banks to automate many tasks, but it has also reduced the availability of data that is used to inform investment decisions. As a result, researchers and analysts in the finance sector are facing significant challenges in accessing high-quality data that can inform their research.

The decline in demand for computer science graduates in finance also has significant implications for research communities. The reduction in the availability of talent in the sector is expected to reduce the number of research papers being published, thereby reducing the availability of new knowledge and insights. This trend is particularly significant for industries that rely heavily on data, such as finance and healthcare. For example, the use of AI-powered technologies has enabled researchers to analyze large datasets, but it has also reduced the availability of data that is used to inform research. As a result, researchers in the finance sector are facing significant challenges in accessing high-quality data that can inform their research.

The decline in demand for computer science graduates in finance is part of a larger trend that is shaping the data sources domain. The use of AI has become increasingly prevalent in many industries, including finance, healthcare, and education. The use of machine learning algorithms and natural language processing has enabled many organizations to automate tasks, but it has also reduced the availability of data that is used to inform research and decision-making. This trend is particularly significant for industries that rely heavily on data, such as finance and healthcare. For instance, the use of AI-powered technologies has enabled banks to automate many tasks, but it has also reduced the availability of data that is used to inform investment decisions.