AI, Jobs and Skills: Why the Future of Work Will Be Built Around Problems – La Prealpina
My analysis on artificial intelligence and the future of work has also been published by La Prealpina. The starting point is simple: we are still debating whether AI will destroy or create jobs, while something potentially more important is already happening. The boundaries between professions are becoming increasingly permeable.
AI allows individuals to move across tasks, skills, and disciplines that until recently belonged to different professional roles. This does not make expertise obsolete. It changes the way expertise is combined — and may ultimately change what we mean by a “job”.
Read the full article here.
TALKING POINTS
- AI does not know professions. It knows problems.
- The traditional boundaries between jobs and skills are becoming increasingly permeable.
- AI enables individuals to perform tasks that previously required different specialists.
- Work does not disappear. It decomposes and recombines.
- Expertise remains essential, but the ability to connect different forms of expertise becomes more valuable.
- AI lowers the cost of complexity and expands access to cognitive capabilities.
- AI distributes tools, not talent.
- Judgement, critical thinking and responsibility may become more valuable as execution becomes easier.
THE DEBATE ABOUT JOBS IS TOO NARROW
Will artificial intelligence destroy jobs or create new ones? It is an important question, but it risks keeping us focused on the labour market of yesterday. Technological revolutions have always changed what humans do. AI may go further by changing how we organize what humans do. A salesperson can build a website. An engineer can develop a financial analysis. A researcher can produce communication materials. These examples are interesting not because AI has suddenly made everyone an expert in everything. It has not. They matter because activities that were once separated by professional boundaries can increasingly be combined by the same individual.
DO NOT MISS MY TALK IN WARSAW: TIME TO PERSONALIZE PREVENTION?
FROM SPECIALIZATION TO CONNECTION
For more than two centuries, economic progress has been closely associated with specialization. We created professions, departments, faculties, and job descriptions around increasingly specific forms of expertise. AI does not eliminate the advantages of specialization. But it makes its borders more permeable. When faced with a task, AI does not need to respect the organizational distinction between marketing, finance, research, or communication. It helps address the problem by drawing on capabilities across those domains. This leads to a simple idea:
AI does not know professions. It knows problems. And humans working with AI may increasingly begin to organize their own work in the same way.
DO NOT MISS THIS ANALYSIS: AI AND THE FUTURE OF WORK
WORK DOES NOT DISAPPEAR. IT DECOMPOSES.
This may be one of the most important changes taking place beneath the debate about automation. A job is traditionally understood as a relatively stable bundle of tasks. AI begins to unbundle those tasks.
Some can be automated. Others can be accelerated. Others can be performed by people who previously lacked the technical capability to execute them independently. New combinations then become possible.
Work does not simply disappear. It decomposes and recombines. The interesting question is therefore not only which occupations survive, but which combinations of capabilities become valuable. That puts a premium on something our highly specialized education and labour systems have sometimes undervalued: the ability to move intelligently between disciplines.
AI LOWERS THE COST OF COMPLEXITY
There is also an entrepreneurial dimension. Many activities that once required a relatively large organization — research, analysis, design, coding, translation, communication — are increasingly accessible to smaller teams and individual professionals.
AI therefore reduces what we might call the cost of complexity. This could redistribute some cognitive capabilities that were previously concentrated inside large organizations.
But there is an essential distinction. AI distributes tools. It does not distribute talent. Giving everyone access to powerful cognitive tools does not make everyone equally capable of using them. A telescope allows us to see further. It does not teach us how to understand the sky.
READ IT AGAIN: WHAT KEEPS US STILL
THE MORE AI CAN DO, THE MORE JUDGEMENT MATTERS
This apparent paradox may define the next phase of the AI revolution. The more machines can execute, the more valuable human judgement becomes.
Knowing how to formulate the problem. Asking the right question. Understanding context. Connecting different disciplines. Recognizing when an answer is plausible but wrong. Exercising doubt. Taking responsibility for a decision.
These are not residual skills left to humans because machines cannot yet perform them. They are increasingly the skills that determine how effectively we can use the capabilities machines provide.
The challenge for education is therefore enormous. Preparing people for a predefined profession may no longer be enough. We need to prepare them to learn, adapt, connect knowledge and solve problems that we cannot yet predict.
WHAT IF THE JOB TITLE IS THE WRONG UNIT OF MEASURE?
Perhaps this is where the debate about the future of work should move next. For generations we have asked young people: What do you want to be? Engineer. Lawyer. Journalist. Economist. Perhaps the better question will increasingly become:
What problems do you want to be able to solve?
Professions will not disappear. Expertise will not disappear. Human knowledge will certainly not become irrelevant. But the walls separating these domains may become much lower. For more than two centuries, we organized work around professions.
AI may be reorganizing it around problems.
And if that is true, our greatest mistake would not be failing to predict which jobs AI will eliminate. It would be educating and organizing people for professional boundaries that are already beginning to disappear.
