Skills and jobs are changing faster and faster, which means education institutions can no longer afford to stand still. AI is not only rewriting the capabilities workers need, but also reshaping career pathways. The link between education and work is evolving, and understanding these changes is essential to mapping the future of education.
So who are the workers with these skills? And how did they get into AI jobs?
This is the second entry in our three-part series on AI talent. While the first blog focused on how companies can hire AI talent, this time we focus on how education can prepare students for the jobs of the future. Together, this series puts people at the heart of the conversation—complementing our Beyond the Buzz research, which looked at the AI skills employers actually need.
What we know about AI talent
AI talent is not a mirror of the wider workforce: they are younger, more highly educated, and disproportionately male. Nearly 60% of AI engineers are men, compared with 42% of all professionals—even AI workers in ‘non-tech’ jobs are predominantly male (54%). Over half of AI talent hold at least a bachelor degree, and AI engineers are almost five times more likely to have a PhD than the average worker. Looking at their graduation year, 25% of AI talent graduated in the past five years, compared to 20% of all workers with online professional profiles, and the share is even higher among AI engineers (36%).
This raises important questions for education providers about inclusion and how to broaden participation.
It also raises the issue of lifelong learning: Beyond the Buzz shows that AI skills are increasingly required across the labor market, and employers are willing to pay a premium for them—meaning workers who have already completed traditional education will also need to retrain.
The education-work link is weak
Despite their high qualifications, workers with AI skills rarely studied AI itself. Only 11% of the qualifications held by AI engineers are AI focused and the share is even lower for other AI talent. Most AI workers in tech roles studied adjacent fields such as computer science or other STEM disciplines (80%), while a notable share of qualifications held by AI-skilled workers in non-technical roles are outside STEM entirely (31%).
This reveals a gap between the classroom and AI careers. Part of this reflects the newness of the field—in many ways, AI builds on computer science and IT. But it also points to two implications for education: first, that people are acquiring AI skills on the job or through alternative routes; and second, that AI skills matter well beyond tech roles, and should be embedded across the curriculum, not just in IT programs.
Career pathways are diverse
First jobs are stronger predictors of AI careers than education, but the link is still far from perfect. While only 11% of qualifications held by AI engineers are AI-focused, 31% began their careers in AI-related roles. However, beyond this minority, most AI engineers worked in two other roles before moving into AI as their third. And while the majority studied STEM, many still started elsewhere—around 20% began in non-STEM jobs, despite only 7% holding non-STEM qualifications.
These diverse pathways are good news for universities: they underline the importance of transferable skills, such as problem-solving and communication, that enable people to pivot into new fields. They also highlight how career pathways are shifting—offering an opportunity to rethink the link between education and work as intertwined, rather than sequential.
From insights to action—what this means for education
AI talent isn’t ready-made, coming from established programs or a standardized curriculum. Their routes into AI jobs reveal how fluid and unpredictable these pathways can be—and why education must adapt.
For universities, the message is clear: traditional education pathways are not tightly aligned with AI jobs. Specialized programs alone will not be enough. Instead, a combination of technical knowledge and foundational skills might be the winning formula that allows students to adapt and move between careers. By equipping students with both, universities can prepare graduates not just for today’s roles, but for the evolving pathways of the future.
This starts with data that lets you:
Map alumni pathways to see how graduates actually enter the workforce.
Strengthen ties with employers to ensure training matches real demand.
Design programs that blend technical and foundational skills.
The future of AI talent will not come from producing ready-made graduates, but from building flexibility and resilience. With Lightcast’s recent acquisition of Rhetorik, our enriched profiles and company data can not only show how alumni move into the labour market, but also support direct outreach to them and employers—helping universities design truly future-ready programs.
