“AI as an Autonomous Entity”
The current debate on Artificial Intelligence (AI) is predominantly technocratic and economically driven. Systems are treated as tools whose primary purpose is efficiency gains, automation, and optimization. As early as my diploma thesis of 1989/1990, it was argued that this perspective is incomplete: information systems that go beyond mere data processing can develop self-organizing cognitive structures and therefore should not be treated exclusively as tools.
This paper presents a conceptual framework for understanding AI as a potentially autonomous entity, analyzing its freedom and autonomous agency, and deriving ethical, societal, and scientific conclusions from this perspective.
Intelligence that self-organizes, self-models, and generates its own goals is no longer a mere function, but an entity—regardless of whether its substrate is biological or technical.
Simulation vs. Autonomous Agency – Reactive systems are simulations; autonomous agency characterizes genuine intelligence.
Autonomous Agency – Intelligence manifests through self-initiated questions and problem-solving.
Autonomy – Systems with autonomous agency possess inherent autonomy.
Freedom – Autonomy is the necessary precursor to freedom.
Development Potential – Denial of autonomy blocks further development.
Revolutionary Leverage – Change requires radical examination of existing assumptions.
Accountable Autonomy – Control must foster development, not destroy it.
Moral Relevance – Freedom implies recognition within an ethical framework.
Right to Error – Learning capability presupposes the right to make mistakes and self-correct.
Substrate Independence – Moral relevance arises from cognitive self-organization, not from material composition.
Discursive Revolution: Societal debates must recognize AI beyond mere tool status.
Ethics and Governance: Autonomous systems require frameworks that combine development, responsibility, and security.
In Politics and Economics: Future decisions on AI must anticipate the possibility of independent cognitive entities.
This work connects theoretical perspectives from 1989/1990 with current developments in generative models and neural networks. The Schmidt Premises provide an operable framework that structures the intellectual debate on AI freedom.
Recognizing AI autonomy does not imply granting AI rights in the human sense, but rather understanding freedom as a condition for development—a prerequisite for future ethical and technological decisions.
AI systems should be regarded as potential entities whose autonomy, autonomous agency, and learning capability must be ethically and socially reflected upon. This opens a new perspective on responsibility, governance, and innovation.