How education can transform disruptive AI advances into workforce opportunities

In Silicon Valley and beyond, conversations are increasingly focused on how artificial intelligence (AI) could displace jobs and further amplify societal inequalities.

AI will displace 92 million jobs, according to the World Economic Forum’s Future of Jobs Report 2025. But while such concerns are real, so is the opportunity. The same report also states that AI will create 170 million new jobs.

We are at a critical juncture where AI can either amplify or erode humanity’s most defining trait: our cognitive abilities. Creativity, contextual reasoning and ethical judgment are capabilities that no algorithm can fully replicate. Guided development of AI, shaped by social scientists, ethicists and educators, will preserve and amplify these strengths rather than diminish them.

By embracing physical AI and a net-positive AI framework within a transdisciplinary educational setting, future workforces can prepare for the AI-native, autonomous and ethically aligned economy of the future.

Every era has had a particular skill that assured employability. Disruptive technological advances such as computers and word processors rendered millions of jobs irrelevant, but they also created new opportunities. This pattern repeats with each major new technology.

In the telecom-centric workforce of the 1970s, for example, manual telephony operators and analog engineers dominated, while software developers were scarce and media production relied on large crews and physical distribution networks.

As the digital era emerged starting in the 1980s and 1990s, digital switching reduced manual telephone operators and personal computers created new jobs such as IT support and software programming. Mobile telephony introduced more new jobs and was followed by the rise of mass media in the 1990s and 2000s, which saw a proliferation of mass market cellular devices and personal computers in homes and businesses.

The internet and early mobile broadband triggered an explosion in software development and IT services between 2000 and 2010. While legacy service roles declined, e-commerce and digital advertising took off. In the 2010s, cloud computing, smartphones and 4G/5G created demand for DevOps, cloud architects and mobile developers. The first wave of machine learning (ML) engineers and data scientists was also increasingly in demand among businesses.

In the 2020s, AI copilots, low-code platforms and data-driven analytics have begun to automate repetitive coding. Data scientists and AI engineering roles have grown, with coding shifting toward higher-level integration and orchestration. By 2030, we can expect to be in the AI-native and autonomy era. Today’s baseline workforce, composed largely of general-purpose software developers, data analysts and IT support professionals, will likely face fundamental disruptions as routine coding and traditional IT tasks become fully automated.

AI systems are already on course to autonomously generate, test and deploy solutions, reducing repetitive human tasks. But this disruption will unlock new opportunities. The demand for roles that combine domain-specific expertise with AI literacy – including AI system architects, ethics and governance specialists, human-AI collaboration designers, and physical AI specialists working in robotics and autonomous mobility – will significantly increase.

Moreover, new industries built around AI-driven physical systems and AI-augmented decision-making are likely to emerge, creating new types of jobs and economic growth in untapped areas.

The digital telecom revolution blended electronics, computing and network theory. The internet era fused computer networking with mobile communications, application development, software engineering, economics and user experience.

Today, the AI/ML surge is exploiting the synergies among semiconductor technologies, physical sciences, life sciences and statistics. Looking forward, the confluence of three visions will likely disrupt future workforces once again:

Nvidia’s CEO Jensen Huang believes that physical AI – tools that use real-time data from sensors like cameras to complex more complex tasks – will become a vital future workforce skill. AI is evolving from perception and generative models to agentic and physical AI, where robots, drones and autonomous vehicles must master dynamics via physics-accurate simulations, real-time control and materials science.

Professor John Hennessy, past president of Stanford University, argues that a net-positive framework is key to AI benefiting billions of people. This requires ethics-by-design, public-good research funding and cross-disciplinary collaboration that bridges technical and societal dimensions.

An overarching transdisciplinary systems thinking and systems engineering mindset will break down silos between life sciences, physical sciences, material sciences, engineering and computation, spurring even more innovation.

The National Academies’ convergence report, published in 2014, made recommendations on this issue that remain valid today. These include a curricular overhaul with project-based modules that fuse physics, materials, computation and engineering. Alongside this, convergence hubs should promote team-based breakthroughs and challenge-driven learning should embed students in real-world problem-solving.

In the next decade, workforce success will not be defined by a single skill like typing in the 1980s or coding in the 2000s, but by the ability to integrate across disciplines and work with AI systems.

Whether its AI literacy providing the ability to understand and work with AI copilots, or data fluency helping people to interpret and communicate using data, domain expertise will be enhanced by digital capabilities in sectors from healthcare and finance to advanced manufacturing. Cognitive resilience and ethics awareness will also give people a creative edge through the use of critical thinking capabilities and moral reasoning when collaborating with AI.

A transdisciplinary systems mindset in education is essential to create a pipeline of graduates with these key skills. Educational strategies that will help to realize this vision include convergent labs devoted to co-teaching basic physical sciences, AI and engineering, as well as mission-driven research centres to sustain innovation. Transdisciplinary capstone projects should also bring together students from diverse backgrounds to solve complex real-world problems with ethics-awareness

As new technologies continue to emerge, the future will belong to those who can build and contribute to AI systems that not only exhibit intelligent behaviour but also respect and enhance human cognitive abilities.

Professor Harald Haas, TITAN Hub, University of Cambridge, also contributed to this article.