Google DeepMind top executive prioritises lifelong learning and curiosity in AI hiring

Google DeepMind is placing greater emphasis on lifelong learning, curiosity and interdisciplinary skills when assessing graduate candidates, with qualifications remaining important but no longer serving as the sole indicator of a candidate’s suitability for AI-related roles.

Lila Ibrahim, chief AI readiness officer at Google DeepMind, said that the company increasingly values evidence of learning beyond formal education, including additional qualifications, experiences and skills that demonstrate a commitment to continuous development.

“A single qualification can only tell me what you’ve learned, but multiple qualifications show me that you have a learning mindset,” Ibrahim said.

She noted that graduate CVs increasingly include learning experiences gained outside traditional educational settings, alongside academic qualifications and work experience. For recruiters, these details can offer insights into a candidate’s curiosity and willingness to keep learning throughout their career.

Ibrahim also highlighted the ability to ask meaningful questions as an increasingly important skill in AI-driven workplaces, where access to information and answers is becoming easier.

“The cost of obtaining answers is now very low, but knowing how to ask truly excellent questions is becoming increasingly difficult,” she said.

According to Ibrahim, candidates should demonstrate curiosity, the willingness to embrace unfamiliar and unpredictable challenges, and the ability to work in dynamic environments and collaborate with others.

Why interdisciplinary skills matter in AI

Beyond technical expertise, Google DeepMind is looking for professionals who can bring together different disciplines and work effectively across teams.

Ibrahim said collaboration between specialists in fields such as neuroscience, ethics and biology has contributed to the organisation’s technical breakthroughs. The company has also hired philosophers, whose perspectives can help shape ideas, alongside operational teams that translate them into action.

Her comments align with views expressed by Demis Hassabis, co-founder of Google DeepMind, who has argued that science, technology, engineering and mathematics (STEM) education and computer science remain important, but that the development of AI also requires knowledge of humanities disciplines, including philosophy, economics and ethics.

“I believe we truly need them in the world we are about to enter,” Hassabis said during an interview at a business conference in London in July.

Ibrahim took on the role of chief AI readiness officer in early 2026, after serving as Google DeepMind’s chief operating officer since 2018. She said the role was established to help address the widening gap between the technologies being developed and society’s readiness to adopt them.

AI adoption outpaces workforce skills development

The emphasis on continuous learning comes as organisations continue to expand their use of AI while facing challenges in preparing employees to use the technology effectively.

A recent study cited in the reports, drawing on more than 300 million registered learners on Coursera and Udemy, found that business adoption of AI is advancing faster than the development of workforce skills needed to use it.

Although nearly 75% of organisations had deployed or were piloting AI, only 18% of respondents said most of their employees had participated in AI upskilling or reskilling over the previous year.

The findings point to a growing responsibility for employers and HR leaders to make learning a continuous part of workforce development rather than relying solely on formal qualifications or one-off training programmes.

As AI reshapes roles and the skills required to perform them, Google DeepMind’s hiring priorities highlight the importance of combining technical capabilities with curiosity, adaptability, critical thinking and collaboration. 

These capabilities can help employees navigate changing job requirements and work effectively in environments where humans and AI systems increasingly operate alongside one another.