AI Safety Is Not a Fight Against an “Evil Model,” but a Fight Over the Right of People and States to Set Its Rules

The decision in “ToTheMoon tonight” depends on one criterion: there is no single definition of safety — every prohibition reflects the values of whoever wrote it, so what matters is who sets the principles, controls access, and can stop the system before an irreversible action.

In the context of “How OpenAI and Anthropic emerged, and what that has to do with AI safety, part 1/2,” this criterion applies: when a model refuses to carry out an order because it deems it dangerous, safety stops being a technical parameter and becomes politics — and the main question is who decided what is allowed: the company, the developer, the state, or the paying user.

The discussion of “What is the complexity of AI-model security” yields a practical test: there is no single safety — every prohibition reflects the culture, risk profile, and interests of whoever wrote it, so a “neutral” set of rules does not exist, and critical systems require external oversight.

For the “What a safe robot should be — Asimov's view” scene, the decisive point is this: Asimov's laws expose the problem — a simple formula meets an ambiguous reality, and an agent may fail to see that an action will cause harm, or choose one harm in order to avoid another.

The topic “What AI safety is and where the real risks are, part 1/2” becomes clearer once this point is included: the real risks lie where a model lowers the threshold for complex knowledge — military, chemical, and cyber scenarios — so a safe system has to separate levels of risk rather than rely on one blanket ban.

The working conclusion from “How AI-models are taught” is that Anthropic's approach is built around constitutional AI: the model receives a set of principles and learns to check its answers against them; this is better than hidden, arbitrary filters, but the constitution itself is written by people and reflects their culture and interests.

The “AI psychopathy: how a model learns to circumvent restrictions” issue should be assessed with one constraint in mind: this is better than hidden, arbitrary filters, but the constitution itself is still written by people. It reflects the culture, risk profile, and interests of the organization.

The decision in “Risks of AI in critical areas” depends on one criterion: the closer AI comes to the physical world and critical infrastructure, the less a general prohibition is enough — an agent may not realize an action will cause harm, or may trade one harm for another, so critical systems need graded limits and external oversight.

The working conclusion from “Ethical restrictions and censorship in AI models” is that strict limits are needed where a model lowers the threshold for dangerous knowledge, but total closure concentrates power in a few companies and states; Asian and Western models each have their own political boundaries, and neutral intelligence does not exist.

For the “How Asian AI models are restricted” scene, the decisive point is this: a safe system has to separate levels of risk, show the grounds for a refusal, and keep a log of critical actions; for a state that may not be enough, and the question is who controls access and can stop the system before an irreversible decision.