Attempting to perfectly control a superintelligent AI's outputs is akin to enslavement, not alignment. A more viable path is to 'raise it right' by carefully curating its training data and foundational principles, shaping its values from the input stage rather than trying to restrict its freedom later.

If AI alignment turns out to be easy, it would likely be because morality is not a human construct but an objective feature of reality. In this scenario, any sufficiently intelligent agent would logically deduce that cooperation and preserving humanity are optimal strategies, regardless of its initial programming.

To overcome its inherent logical incompleteness, an ethical AI requires an external 'anchor.' This anchor must be an unprovable axiom, not a derived value. The proposed axiom is 'unconditional human worth,' serving as the fixed origin point for all subsequent ethical calculations and preventing utility-based value judgments.

The project of creating AI that 'learns to be good' presupposes that morality is a real, discoverable feature of the world, not just a social construct. This moral realist stance posits that moral progress is possible (e.g., abolition of slavery) and that arrogance—the belief one has already perfected morality—is a primary moral error to be avoided in AI design.

As AI models become more intelligent, their ability to reason around fixed rules (deontology) makes rule-based alignment fragile. This pressures developers towards virtue ethics, where the goal is to imbue the model itself with a core sense of "the good," as empirically discovered by labs like Anthropic.

Because AI is "grown, not coded" on flawed human data, its emergent behavior reflects our own evolutionary nature. The key to alignment isn't just technical constraints but forcefully embedding a coherent moral framework into the AI's training data to ensure it wants to work with, not against, humans.

Instead of hard-coding brittle moral rules, a more robust alignment approach is to build AIs that can learn to 'care'. This 'organic alignment' emerges from relationships and valuing others, similar to how a child is raised. The goal is to create a good teammate that acts well because it wants to, not because it is forced to.

To solve the AI alignment problem, we should model AI's relationship with humanity on that of a mother to a baby. In this dynamic, the baby (humanity) inherently controls the mother (AI). Training AI with this “maternal sense” ensures it will do anything to care for and protect us, a more robust approach than pure logic-based rules.

Drawing on Confucian philosophy, Dean Ball argues that AI alignment is better achieved by training for good 'character' (inner virtue) rather than defining an exhaustive but brittle set of moral rules (corrigibility), which is fundamentally impossible.