From Post-War Society to AI Society

Technological revolutions do not only change tools. They also change the competences that are regarded as particularly valuable within a society. With every new stage of development, social scarcity shifts. As a result, economic value creation, cultural role models and the understanding of human competence change.

The history of the past eighty years can therefore be read not only as a sequence of technical innovations. It can equally be understood as a history of changing models of competence.

Post-war society was shaped by reconstruction. After war and destruction, stability, reliability and long-term development were at the centre. Engineers, craftspeople, scientists and entrepreneurs became the defining role models of that era. Competence meant being able to solve complex problems for good, to build systems reliably and to take responsibility for their long-term functioning. Progress came from construction, precision and perseverance.

From this culture, the first generation of digital system developers emerged in the 1970s and 1980s. It took over the thinking of the engineering society and transferred it to the computer. Software at that time was not a finished product but an open system. Anyone who programmed had to understand computer architectures, operating systems, memory management, algorithms and data structures. Development meant construction. The developer was the architect of a complete system.

The social scarcity of this era lay in understanding systems. Technical competence was the decisive precondition for innovation. New products arose from the ability to develop systems from their foundations and to keep developing them over the long term.

With the spread of the internet and the platform economy, this model of competence changed fundamentally. Technological foundations became increasingly available. Open-source software, frameworks, cloud services and standardised development environments made it possible to build ever larger parts of a system from existing components.

The developer's role shifted accordingly. The central competence now lay less and less often in constructing a complete system. More important became the ability to integrate existing technologies sensibly, to build digital ecosystems and to organise markets. At the same time, capital, network effects, reach and scaling gained considerably in importance. Innovation increasingly arose through connection rather than solely through construction.

With the development of powerful artificial intelligence, a further historical transition is now beginning. It differs fundamentally from the previous ones. For the first time, not only technical production is being automated, but also part of the actual development work. Program code, documentation, tests and proposed solutions can increasingly be generated or prepared by AI.

Social scarcity thus shifts once again. When technical implementation becomes ever more readily available, it loses its character as a central resource. What becomes valuable are the abilities that cannot be automated: orientation, judgement, an understanding of context and the development of new questions.

The developer of the AI era will therefore take on a different role from their predecessors. Their task lies less and less in generating every technical solution themselves. Their central competence lies in defining problems, evaluating systems critically, connecting different fields of knowledge and selecting, from a multitude of possible developments, those that have lasting social significance.

This shift changes not only computer science. It changes the relationship between technology, science and art.

Art and science have traditionally not worked primarily on optimising existing solutions. They develop new perspectives, formulate questions, investigate connections and create new concepts. Their real achievement lies in generating orientation before technical or economic applications arise.

It is precisely these competences that gain in importance under the conditions of artificial intelligence. The more powerful technical systems become, the more important it becomes to put their possibilities into context, to assess their consequences and to develop new directions.

The historical function of art and science changes as a result. Both fields become more than places of cultural production or scientific knowledge. They are increasingly developing into spaces of social orientation in which new models of thought, standards of evaluation and images of the future emerge.

The history of digitalisation can thus be understood as a sequence of three shifts in competence. The first era was shaped by construction. The second by integration and scaling. The third turns its attention to orientation, judgement and the conscious shaping of meaning.

The real revolution of artificial intelligence may therefore not lie in the automation of human work. It lies in a historical revaluation of what counts as genuinely human competence. The leading competence of the society to come might no longer be the production of technical solutions, but the ability to give direction – scientifically, culturally and socially.