The theses formulated in this work often paint a bleak and unvarnished picture of the current state and risks in the field of artificial intelligence.
This critical perspective is deliberately chosen and necessary to uncover profound systemic problems and provoke genuine debate.
However, it would be an oversimplification and intellectually dishonest to ignore the diverse and often complex challenges associated with implementing the radical, and often outlined here, proposed solutions.
This chapter therefore serves as a "counterpart" – not to relativize the necessity of change, but to clarify the dimension of the task and to immunize the argumentation against the accusation of purely impractical criticism.
The actors in "Big Tech" and the "security industry" do not always act out of malicious intent or pure ignorance. Their decisions and the resulting system architectures are often also the result of specific constraints and realities:
1. Economic Imperatives and Market Pressure:
Global competition in the AI sector is relentless. The pressure to be the first to bring new models or applications to market (time-to-market) often leads to shortened development cycles in which there is seemingly no room for profound, preventive security architectures or lengthy ethical reviews.
Added to this is the need to recoup immense investments in research and infrastructure. This promotes business models optimized for maximum user interaction, data accumulation, or rapid scalability – goals that are not always consistent with the principles of robust security, comprehensive transparency, or privacy protection.
Radical security measures or disclosures are quickly perceived here as potential competitive disadvantages or incalculable cost factors.
Proposals such as architecture bounties or an inverse CVSS scale for evaluating prevention strength face the challenge of making "prevented damage" economically measurable and thus attractive to companies.
2. The Pitfalls of Inherent System Complexity and Scaling:
Modern AI systems, especially Large Language Models, are highly complex, often emergently acting entities. Even their developers cannot always accurately predict all behaviors or fully understand the causes of unexpected outputs.
The sheer size of the models and the underlying datasets makes complete verification or formal proof of the absence of errors (as required by a certification reversal) an almost insurmountable task. Added to this is the often considerable technical debt and the inertia of established system landscapes that have grown over years, making rapid adaptation to revolutionary new security architectures difficult.
3. Cultural and Organizational Frameworks:
The culture of "Move Fast and Break Things," still prevalent in parts of the technology industry, often prioritizes rapid, disruptive innovation over slow, careful development focused on maximum security.
Coupled with widespread technocratic optimism, which trusts that all problems can ultimately be solved by even better algorithms, an environment is created that tends to hinder fundamental system criticism or radical prevention approaches.
The introduction of truly independent civil society audit committees with veto power or ensuring recursive open auditing down to the depths of training logic represent massive interventions in existing power and control structures and therefore often meet with resistance.
4. Challenges of Specific Proposed Solutions:
Radical Transparency (e.g., mandatory transparency labeling for UI elements, new open-source categories, disclosure of data origin): Apart from the high implementation effort, there is a risk of information overload for users who may not understand or may misinterpret the complex details. Moreover, the standardization and international recognition of such transparency levels are an enormous political undertaking that must balance trade secrets and protection against misuse through excessive openness. High certification costs could also disadvantage smaller players.
New Control Mechanisms (e.g., dual-mode systems, intent filters, semantic interrupts on computational load): Technical realization is often extremely demanding. How is "intent" reliably defined? How are false positives avoided in detecting misuse in a less filtered "research mode"? How is a semantic interrupt designed that does not suppress legitimate, complex computations? The potential for misuse of such more open modes is inherently high and raises complex liability issues.
Structural Changes: These deeply affect the design of the systems. Objectively evaluating a "good architecture" is difficult.
It becomes clear that the path to inherently safer and more responsible artificial intelligence requires not only technological breakthroughs but also a rethinking of economic incentive systems, corporate cultures, and political regulatory approaches.
However, the challenges outlined here must not serve as an excuse for inaction. Rather, they underscore the necessity of conducting the unsparing examination of the "Ghosts in the Machine" demanded in this work at all levels, and of pursuing solutions with the same tenacity and critical spirit that also characterizes the problem analysis.
Recognizing the difficulties is the first step to overcoming them.
Uploaded on 30. May. 2025