Google DeepMind is putting greater emphasis on how candidates continue to learn, build experience across disciplines and work with others in uncertain environments. Degrees remain an important part of a résumé, but these broader capabilities are increasingly influencing hiring decisions for researchers, engineers and other roles, Chief AI Readiness Officer Lila Ibrahim said at a media lunch in September.
Ibrahim said a single degree generally shows what a candidate has studied. Multiple qualifications, additional courses and experience outside the classroom can offer a clearer indication of a person’s willingness and ability to keep learning. She has observed that recent graduates are increasingly listing projects completed outside formal education, along with skills developed alongside their academic and professional experience.
Multiple learning paths offer a clearer signal
For Ibrahim, recruiters are not simply adding more credentials to the screening process. They are looking for evidence of curiosity and the ability to adapt to continuous learning. As AI tools make it easier to retrieve information and generate content, she said, the ability to ask high-quality questions is becoming more difficult to find.
“Many people do not really learn at school how to ask questions, how to ask good questions, or how to keep asking why something is the way it is,” Ibrahim said. As AI takes on more information retrieval and content-generation tasks, the ability to understand and define a problem, then test the answer, may become a more important differentiator in how people work with these tools.
The qualities Google DeepMind values include curiosity, the confidence to take on unfamiliar and unpredictable tasks, the ability to operate in a changing environment and a willingness to collaborate. The criteria extend beyond technical positions to roles that connect research, product development and organisational execution.
DeepMind links technology with the humanities
Ibrahim became Google DeepMind’s chief operating officer in 2018 and moved into the chief AI readiness officer role in early 2026. She said the position was created to address a widening gap: AI development is moving faster than society, businesses and workers can adapt to the technology.
Google DeepMind founder Demis Hassabis has previously said that computer science and other STEM disciplines remain important, while the next phase of AI development will also require contributions from fields such as philosophy, economics and ethics. Ibrahim said collaboration among neuroscientists, ethicists and biologists has already helped DeepMind advance some of its technical work.
She also said the company has hired several philosophers. Their perspectives, however, need to be combined with operational staff capable of carrying ideas through to execution before they can translate into action.
Ibrahim said the UK is currently well placed to find this type of interdisciplinary talent. Compared with her experience working in Silicon Valley more than 30 years ago, she said the UK places greater value on philosophy, politics and related capabilities, producing a more varied mix of professional backgrounds.
AI deployment is outpacing employee training
The hiring discussion also highlighted the gap between corporate AI adoption and workforce preparation. As AI becomes part of everyday work, concerns about large-scale job losses have eased somewhat, while attention has shifted toward how roles will be redefined and how companies and human-resources teams can combine human capabilities with AI skills.
A study covering more than 300 million registered learners on Coursera and Udemy found that companies are deploying AI faster than employees are learning how to use it effectively. Nearly 75% of organisations have deployed or are piloting related technologies, but only 18% said that most employees had taken part in AI upskilling or retraining programmes during the past year.
For Google DeepMind, that gap points to simultaneous changes in hiring standards and internal training. Degrees still provide a baseline signal of capability, but a record of continuous learning, experience working across disciplines and the ability to frame strong questions are increasingly being used to assess whether candidates can contribute effectively in an AI-enabled workplace.