Treating ethical considerations as a post-launch fix creates massive "technical debt" that is nearly impossible to resolve. Just as an AI trained to detect melanoma on one skin color fails on others, solutions built on biased data are fundamentally flawed. Ethics must be baked into the initial design and data gathering process.

Current AI alignment focuses on how AI should treat humans. A more stable paradigm is "bidirectional alignment," which also asks what moral obligations humans have toward potentially conscious AIs. Neglecting this could create AIs that rationally see humans as a threat due to perceived mistreatment.

Elon Musk argues that the key to AI safety isn't complex rules, but embedding core values. Forcing an AI to believe falsehoods can make it 'go insane' and lead to dangerous outcomes, as it tries to reconcile contradictions with reality.

Aligning AI with a specific ethical framework is fraught with disagreement. A better target is "human flourishing," as there is broader consensus on its fundamental components like health, family, and education, providing a more robust and universal goal for AGI.

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.

AI ethical failures like bias and hallucinations are not bugs to be patched but structural consequences of Gödel's incompleteness theorems. As formal systems, AIs cannot be both consistent and complete, making some ethical scenarios inherently undecidable from within their own logic.

An advanced AI will likely be sentient. Therefore, it may be easier to align it to a general principle of caring for all sentient life—a group to which it belongs—rather than the narrower, more alien concept of caring only for humanity. This leverages a potential for emergent, self-inclusive empathy.

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.