By 2030, it’s projected that 70 percent of the skills used in most jobs will change , with AI acting as the driving force behind this transformation. For workers, this means that two-fifths (39 percent) of their existing skill sets will be transformed or become outdated over the 2025-2030 period .
As industries and job roles evolve, the skills needed to succeed are shifting rapidly. Talent must adapt to stay competitive in the workforce, and higher education must adapt to meet industry needs and remain the top provider for training talent.
This shift is happening on the front lines of hiring today. We recently spoke to a university president who is concerned that computer science graduates, who had rigorous majors in anticipation of high-skill, stable positions, will struggle to secure jobs. What can computer science departments do in the near term to make sure that the curriculum, capstones, and experiential-learning experiences are enough so that this year’s graduating class is prepared to succeed in a changing job market—just six months from now?
A recent report by Lightcast reveals that the tech sector is already experiencing the most significant disruption. From 2021 to 2024, tech occupations have seen the sharpest skill transformations, with computer scientists at the forefront. While the overall demand for the role of computer scientists has not fluctuated much over the past few years (~7,000 national job postings in the year 2021 compared to ~7,000 job postings in 2024), what has changed is the skills required to fill these positions. In fact, the disruption index for computer scientists is strikingly high at 94, a dramatic leap compared to similar occupations like software engineers, at 68.2 , a stark difference in the skills that are now deemed essential for computer scientists compared to just a few years ago.
So, what exactly has changed? Foundational skills like programming, computer science, algorithms, and statistics remain critical, but new technical skills have emerged in addition to the basics. Today, GenAI, large language modeling, AI, ML, and natural language processing are vital for computer scientists to stay relevant in the field, with additional programming languages like R.
But it’s not just new technical skills that are in demand for computer scientists. Durable skills have also evolved in response to AI; there is now an increasing demand for skills that support adaptability and change, like research, innovation, and being energetic. This shift moves away from the more traditional, technical durable skills like safety assurance and troubleshooting. While job postings offer a snapshot of current demand, they don’t capture the full picture of where the field is heading, especially with AI disruption accelerating at an unprecedented pace.
At a recent Council on Foreign Relations event, Dario Amodei, CEO of Anthropic, predicted that within three to six months, AI could be writing 90 percent of all code . He described how programmers’ roles will shift toward defining code conditions, structuring design, and managing collaboration. But these tasks, over time, may also become automated.
As we look ahead, the challenge for workers, employers, and higher education will be to anticipate change—deciding where to reinvest, upskill, or reskill the current workforce to meet changing demands and manage uncertainty. More investment will be needed in continuous learning and professional development and more frequent updating of undergraduate and career-launch programming, especially in technology sectors that are reliant on these constantly changing technologies.
And while tech occupations are at the forefront of this transformation, they are just the beginning. We need to create the agile mechanisms that enable employer signaling and embedded work-integrated learning experiences. These changes are part of why at the Business-Higher Education Forum, we are launching our AI and the Future of Talent Collaborative to make the real-time connection between the workforce and higher education.
