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Salary, Impact or Learning? Mantu Experience in Barcelona, 15/10/2026
What should you prioritize at the beginning of your career? That question opened the second edition of Mantu Experience, held in Barcelona on October 15. Where the first edition, in Paris, looked at how AI is reshaping the value of early-career work, this one turned the lens inward: how do you actually weigh salary against impact against learning, when all three pull in different directions? Prevent the problem, don't just treat it Jacques Reynaud opened the session with the case of Florence Nightingale, the 19th-century British nurse, to illustrate a mental model called Inversion. Nightingale did more than treat wounded soldiers. She identified that preventable disease was killing most of them, then used data and visual storytelling to make that fact impossible for decision-makers to ignore.Her method followed a sequence known as Force Field Analysis: identify the problem, define the objective, map the forces supporting change and the forces resisting it, then build a strategy. By turning the question from "how do I treat this?" into "how do I prevent this?", Nightingale dealt with the cause rather than the symptoms. The same reflex applies to the choice between salary, impact and learning: before asking which offer looks best today, ask what could go wrong with each option, then work backward from there. Not every decision deserves the same speed A question every student eventually faces: how fast should you actually decide? The session introduced a matrix plotting decisions by two axes: how consequential they are, and how reversible. Moving small furniture sits at one extreme, low stakes, easily undone. Choosing where to work sits much closer to the opposite corner, consequential and hard to reverse.The rule that followed: decide ASAP when a choice is low-stakes and reversible. Save the "ALAP" approach, as late as possible, for decisions that are consequential and hard to undo, because more time usually means more information. A first job offer built mainly around salary can feel urgent to accept. The matrix suggests the opposite: this is exactly the kind of decision that deserves more time, not less, precisely because it is hard to walk back. Does the tool decide, or do you? Another questionthe first Mantu Experience had already raised in Paris: what does AI actually change? Here, the framing shifted. Rather than asking what AI can do, the session asked who stays accountable for the decision itself, introducing the idea of AI as "Augmented Impact" rather than artificial intelligence.A decision-making template structured the exercise: frame the problem in simple words, name the real alternatives, separate what you'd like to happen from what's actually likely, then commit to a review date to assess the decision's quality later. AI can compare salaries, map career paths, and surface options in seconds. What it cannot do is decide how much weight to give salary over learning, or learning over impact. That judgement stays with the person making the choice. A round table on what actually changes a career The day closed with a round table bringing together Director of Operations at Amaris Consulting in Barcelona Manuel Rebollo, Associate Professor at Universitat Pompeu Fabra and Affiliated Research Fellow at the Max Planck Institute for Innovation and Competition Fabian Gaessler, and Assistant Professor and Ramón y Cajal fellow at Universitat Pompeu Fabra Theodor Vladasel.Between them, the three bring a combination rarely found on the same stage: day-to-day responsibility for hiring and developing hundreds of consultants, and academic research into how careers, decisions, and labour markets actually work. One side lives the consequences of early-career choices inside a real organisation. The other studies the patterns behind them, at scale, across industries. That mix gave the round table a rare quality, grounded enough to speak to a student's actual situation, and rigorous enough to go beyond personal anecdote.The discussion brought the frameworks back to the opening question directly: what to prioritize first, where priorities shift once a career is underway, and what students should ask before accepting a job, when salary, impact, and learning rarely all point the same way.This was only the second edition. Explore all of our opportunities here.


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