What this episode is about
Companies that are adopting AI have started cutting junior roles: fewer interns, fewer entry-level positions, fewer first jobs. Carmen Palomino runs Fundación Universidad Empresa (FUE), the organisation that has spent 50 years working at the exact point where universities, companies and young talent meet in Spain. From that vantage point she is blunt: this is no longer a hypothesis, it is already happening in the large companies that are furthest ahead with AI. And it rests on a conceptual mistake.
The conversation with Juanjo Mestre covers the demographic math that makes the mistake expensive, why the education system has not failed (which is precisely the problem), why no education model works anymore, and the skills the machine does not have.
Juniors are the pipeline, not cheap labour
The reasoning inside many companies goes like this: AI can now do the work we used to give juniors, so we can save ourselves the juniors. Carmen’s answer is that juniors were never there to do the cheap work. You hire juniors because they are your talent pipeline: the people who absorb your values and your ways of working, and who become the seniors you will need later.
In Spain the numbers make this urgent. Over the next ten years around 5.3 million people will retire, and only about 1.6 million young people will enter the labour market. If you do not start bringing them in now, there is simply no place your future seniors can come from. What has to change is the model of incorporation: juniors should not spend their days on tasks AI already does, and companies need to redesign onboarding around where they want that person to be in a year. FUE has just launched a survey on how AI is affecting junior hiring, precisely because the risk is leaving a whole generation stranded.
A notary that certifies degrees
Has the education system failed, then? Carmen refuses the framing. The system is doing exactly what it was designed to do: transmit knowledge and certify it. That design is the problem. Companies no longer want a university that acts as a notary, confirming which degree someone holds. They want graduates who know how to learn, because everything moves too fast for any specific body of knowledge to stay valuable.
The paradox is that companies still ask for degrees, and Carmen explains why: a degree is the only measurement instrument they have, since nobody has properly figured out how to measure competences yet. The university’s real value lies elsewhere anyway: learning how to learn, resilience, connection, networking. After nine centuries it will not die, but it does need to rethink itself.
No education model works anymore
Juanjo pushes on the obvious alternative: is the American model the answer? Carmen has looked at the data and does not buy any of the existing options. The US has youth unemployment levels it had never seen. China bet everything on STEM and this year has around 16% urban youth unemployment. The German dual model is struggling too, because the business model it feeds into is itself changing. Her conclusion: we need a new model, and nobody has designed it yet.
Part of the obstacle is structural. In Spain, getting a new curriculum approved takes two years, plus one to launch and four to teach: the first graduate of a programme designed today arrives in seven or eight years. Companies cannot wait that long, and universities are given no faster option.
The skills the machine does not have
Where should education point instead? At the abilities AI cannot replicate: critical thinking, empathy and creativity. These are not transmitted in lectures; they are acquired through experience, through debate, through what Carmen calls artisanal work as opposed to the degree factory we have built. Inside companies, the equivalent shift is turning tutors into mentors: put a senior next to a junior and, in her words, they make magic together.
She also pushes back on the narrative young people are absorbing, that companies exist to exploit them. Purpose can be exercised in a company as much as anywhere else, and the discourse of good guys versus bad guys is breaking the relationship between young talent and business. Her closing advice to both sides is symmetrical: companies must invest in juniors even when it feels slow, and young people should look for the place that pushes them to keep learning. You can hear related arguments in 1x04 on AI and thinking and 1x02 on AI and the fear of change.
If your team is rethinking how it brings in and develops talent in the age of AI, you can also book a demo with Dcycle and see how we work.