Christian Bosshard, co-founder of MMG Management Consulting, is writing his dissertation on how generative AI boosts our performance, while our own expertise may lag behind. In a conversation with Jakob Barandun on the CapricornConnect podcast, he explains why this “decoupling effect” is becoming a problem right now for an entire generation of young professionals.
Christian Bosshard has been working at the intersection of business, IT, and operations for over 15 years. Together with his partners, he has built MMG Management Consulting from the ground up to a staff of about 30. In addition to his operational work, he is currently exploring a research question that extends far beyond his day-to-day business: What happens to our expertise when visible performance skyrockets thanks to AI, but our own learning process simultaneously withers away?
The Decoupling of Performance and Competence
The term Bosshard coined for this phenomenon is the “decoupling effect,” which refers to the disconnect between performance and the development of competencies. He illustrates this using his own professional history: When he received his first assignment 20 years ago as a new graduate—to conduct an analysis of e-banking users’ behavior—he had to painstakingly acquire knowledge, seek feedback, and repeat his attempts. A young employee given the same task today can produce a report within 20 minutes that already meets senior-level standards.
At first glance, it’s a success story. Studies show, however, that the learning effect comes under massive pressure—and in some cases, it disappears entirely. Bosshard also observes a dangerous conflation: Young professionals increasingly believe they understand a topic, even though it is actually the AI that provides the understanding, not them.
The Lost Sandbox
In the past, according to Bosshard, there was a kind of “sandbox” (a protected practice environment where one has to experiment, fail, and acquire knowledge on one’s own, without a ready-made solution from the start), in which one inevitably had to grapple with a specific domain in order to be able to deliver anything at all. Today, AI delivers results that even its users often can no longer understand in detail. If you ask how two AI-generated answers differ, an explanation is often lacking.
Yet it is precisely the combination of domain knowledge and the use of AI that creates real impact. Those starting their careers who never build the necessary subject-matter expertise will never be able to fully tap into AI’s potential, because they lack the foundation to build on the answers and contribute new insights.
Efficiency or Learning—A Daily Choice
Bosshard illustrates the dilemma using an everyday example: He writes an email, has it revised by AI, and usually gets a better version back. This leads to two paths. The path of efficiency means providing only keywords in the future, since the result will be good anyway. The path of learning involves analyzing what the AI did better and using that to improve one’s own writing. In a fast-paced work environment, Bosshard says, most young people opt for efficiency. That’s understandable, but risky in the long run.
New Tasks Instead of Fewer Tasks
Bosshard does not believe that young talent will therefore become redundant. Rather, he says, new career paths are needed. Traditional entry-level tasks, such as creating reports or PowerPoint presentations, are increasingly being phased out. Instead, younger employees should take on responsibility earlier, work more closely with customers, and tackle concrete problem-solving tasks together with experienced colleagues and AI—not alone, but as part of a team with supervisors and experts.
When critical thinking becomes a scarce commodity
Bosshard sees critical thinking itself as a key risk. Faced with a flood of information that is virtually impossible to verify, trust is increasingly shifting from traditional sources to AI, which is not infallible itself. This is precisely why it is so important for young people to develop their own expertise and critical thinking skills, and why businesses and society must work together to find ways to promote this.
His worst-case scenario: Companies deliberately choose not to invest in the next generation of talent because AI-generated results seem good enough anyway. The result, in three to five years, would be a depleted pipeline of experts and a growing dependence on AI whose accuracy no one can assess anymore. For Bosshard, this leads directly to the question of AI governance: Who is in charge—humans or AI—and to what extent?
Humanity as a Distinguishing Feature
When asked which jobs will disappear, Bosshard offers a nuanced answer: It’s not entire professions that will vanish, but rather clusters of tasks. Anything routine—standard reports, simple analyses, or presentations—can be automated. What remains—and is gaining in importance—are responsibility, creativity, context, expertise, and empathy. After years of digitizing and standardizing customer interactions, Bosshard sees humanity as a unique selling point for companies in the future.
In conclusion: Pause for a moment and keep an open mind
When asked about his dream superpower, Bosshard answers without hesitation: the ability to stop time in order to consciously create space for focus and reflection in the fast-paced world of AI—and to do so occasionally away from technology. His life motto sums up the essence of the conversation: You never stop learning, and you’re never too old to change your mind. Especially in an age of conflicting information and opinions, this openness is more important than ever.
Looking ahead, Bosshard is focusing on the positive: AI as a catalyst for creativity that makes it possible to bring ideas to life that simply lacked the time to be realized until now.
Binci Heeb
See, hear, and read more: “No Risk, No Fun”: How Zation Is Becoming a Global Player Without Investors, with Michael Altenberger, CEO of Zation
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